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github.com-robmarkcole-satellite-image-deep-learning_-_2022-10-18_20-24-04

by: robmarkcole

Publication date: 2022-10-18

Topics: GitHub, code, software, git

Deep learning with satellite & aerial imagery

This repository lists resources on the topic of deep learning applied to satellite and aerial imagery. To a lesser extent classical machine learning techniques are listed, as are topics such as cloud computing and model deployment. You can follow me on Twitter and join the dedicated satellite-image-deep-learning group on LinkedIn. If this resource is useful to your work please consider sponsoring it with a donation via Github Sponsors

How to use this repository: if you know exactly what you are looking for (e.g. a dataset name) you can Control+F to search for it in the page. Otherwise refer to the table of contents below, and search within the relevant section. Material that is suitable for getting started with a topic is tagged with the 🔰 emoji, which can also be searched.

Techniques

This section explores the different deep and machine learning (ML) techniques applied to common problems in satellite imagery analysis. Good background reading is Deep learning in remote sensing applications: A meta-analysis and review

Classification

The classic cats vs dogs image classification task, which in the remote sensing domain is used to assign a label to an image, e.g. this is an image of a forest. The more complex case is applying multiple labels to an image. This approach of image level classification is not to be confused with pixel-level classification which is called semantic segmentation. Read my blog post A brief introduction to satellite image classification with neural networks* Land classification on Sentinel 2 data using a simple sklearn cluster algorithm or deep learning CNN 🔰* Land Use Classification on Merced dataset using CNN in Kerasor fastai. Also checkout Multi-label Land Cover Classification using the redesigned multi-label Merced dataset with 17 land cover classes 🔰* Multi-Label Classification of Satellite Photos of the Amazon Rainforest using keras or FastAI 🔰* Detecting Informal Settlements from Satellite Imagery using fine-tuning of ResNet-50 classifier with repo* Land-Cover-Classification-using-Sentinel-2-Dataset -> well written Medium article accompanying this repo but using the EuroSAT dataset* Land Cover Classification of Satellite Imagery using Convolutional Neural Networks using Keras and a multi spectral dataset captured over vineyard fields of Salinas Valley, California* Detecting deforestation from satellite images -> using FastAI and ResNet50, with repo fsdldeforestationdetection* Neural Network for Satellite Data Classification Using Tensorflow in Python -> A step-by-step guide for Landsat 5 multispectral data classification for binary built-up/non-built-up class prediction, with repo* Slums mapping from pretrained CNN network on VHR (Pleiades: 0.5m) and MR (Sentinel: 10m) imagery* Comparing urban environments using satellite imagery and convolutional neural networks -> includes interesting study of the image embedding features extracted for each image on the Urban Atlas dataset. Accompanying paper* RSI-CB -> A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data. See also Remote-sensing-image-classification* NAIPPoolDetection -> modelled as an object recognition problem, a CNN is used to identify images as being swimming pools or something else - specifically a street, rooftop, or lawn* Land Use and Land Cover Classification using a ResNet Deep Learning Architecture -> uses fastai and the EuroSAT dataset* Vision Transformers Use Case: Satellite Image Classification without CNNs* WaterNet -> a CNN that identifies water in satellite images* Road-Network-Classification -> Road network classification model using ResNet-34, road classes organic, gridiron, radial and no pattern* Scaling AI to map every school on the planet* Landsat classification CNN tutorial with repo* satellite-crosswalk-classification* Understanding the Amazon Rainforest with Multi-Label Classification + VGG-19, Inceptionv3, AlexNet & Transfer Learning* Implementation of the 3D-CNN model for land cover classification -> uses the Sundarbans dataset, with repo. Also read Land cover classification of Sundarbans satellite imagery using K-Nearest Neighbor(K-NNC), Support Vector Machine (SVM), and Gradient Boosting classification algorithms which is by the same author and shares the repo* SSTN -> PyTorch Implementation of SSTNs for hyperspectral image classifications from the IEEE T-GRS paper "Spectral-Spatial Transformer Network for Hyperspectral Image Classification: A FAS Framework." Demonstrates a novel spectral-spatial transformer network (SSTN), which consists of spatial attention and spectral association modules, to overcome the constraints of convolution kernels* SatellitePollutionCNN -> A novel algorithm to predict air pollution levels with state-of-art accuracy using deep learning and GoogleMaps satellite images* PropertyClassification -> Classifying the type of property given Real Estate, satellite and Street view Images* remote-sense-quickstart -> classification on a number of datasets, including with attention visualization* Satellite image classification using multiple machine learning algorithms* satsense -> a Python library for land use/cover classification using classical features including HoG & NDVI* PyTorchUCMercedLandUse -> simple pytorch implementation fine tuned on ResNet and basic augmentations* EuroSAT-image-classification -> simple pytorch implementation fine tuned on ResNet* landcoverclassification -> using fast.ai on EuroSAT* IGARSS2020BWMS -> Band-Wise Multi-Scale CNN Architecture for Remote Sensing Image Scene Classification with a novel CNN architecture for the feature embedding of high-dimensional RS images* image.classification.on.EuroSAT -> solution in pure pytorch* hurricanedamage -> Post-hurricane structure damage assessment based on aerial imagery with CNN* openai-drivendata-challenge -> Using deep learning to classify the building material of rooftops (aerial imagery from South America)* is-it-abandoned -> Can we tell if a house is abandoned based on aerial LIDAR imagery?* BoulderAreaDetector -> CNN to classify whether a satellite image shows an area would be a good rock climbing spot or not* ISPRSS2FL -> code for paper: Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model. S2FL is capable of decomposing multimodal RS data into modality-shared and modality-specific components, enabling the information blending of multi-modalities more effectively* Brazilian-Coffee-Detection -> uses Keras with public dataset* tf-crash-severity -> predict the crash severity for given road features contained within satellite images* ensembleLCLU -> code for 2021 paper: Deep neural network ensembles for remote sensing land cover and land use classification* cerraNet -> contextually classify the types of use and coverage in the Brazilian Cerrado* Urban-Analysis-Using-Satellite-Imagery -> classify urban area as planned or unplanned using a combination of segmentation and classification* ChipClassification -> code for 2019 paper: Deep learning for multi-modal classification of cloud, shadow and land cover scenes in PlanetScope and Sentinel-2 imagery* DeeplearningClassficationLandsat-tImages -> Water/Ice/Land Classification Using Large-Scale Medium Resolution Landsat Satellite Images* wildfire-detection-from-satellite-images-ml -> detect whether an image contains a wildfire, with example flask web app* mining-discovery-with-deep-learning -> code for the 2020 paper: Mining and Tailings Dam Detection in Satellite Imagery Using Deep Learning* e-Farmerce-platform -> classify crop type* sentinel2-deep-learning -> Novel Training Methodologies for Land Classification of Sentinel-2 Imagery* RSSC-transfer -> code for 2021 paper: The Role of Pre-Training in High-Resolution Remote Sensing Scene Classification* Classifying Geo-Referenced Photos and Satellite Images for Supporting Terrain Classification -> detect floods* Pay-More-Attention -> code for 2021 paper: Remote Sensing Image Scene Classification Based on an Enhanced Attention Module* Remote-Sensing-Image-Classification-via-Improved-Cross-Entropy-Loss-and-Transfer-Learning-Strategy -> code for 2019 paper: Remote Sensing Image Classification via Improved Cross-Entropy Loss and Transfer Learning Strategy Based on Deep Convolutional Neural Networks* DenseNet40-for-HRRSISC -> DenseNet40 for remote sensing image scene classification, uses UC Merced Dataset* SKAL -> code for 2022 paper: Looking Closer at the Scene: Multiscale Representation Learning for Remote Sensing Image Scene Classification* potsdam-tensorflow-practice -> image classification of Potsdam dataset using tensorflow* SAFF -> code for 2021 paper: Self-Attention-Based Deep Feature Fusion for Remote Sensing Scene Classification* GLNET -> code for 2021 paper: Convolutional Neural Networks Based Remote Sensing Scene Classification under Clear and Cloudy Environments* Remote-sensing-image-classification -> transfer learning using pytorch to classify remote sensing data into three classes: aircrafts, ships, none* remotesensingpretrainedmodels -> as an alternative to fine tuning on models pretrained on ImageNet, here some CNN are pretrained on the RSD46-WHU & AID datasets* CNNAircraftDetection -> CNN for aircraft detection in satellite images using keras* OBIC-GCN -> code for 2021 paper: Object-based Classification Framework of Remote Sensing Images with Graph Convolutional Networks* aitlas-arena -> An open-source benchmark framework for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO)* droughtwatch -> code for 2020 paper: Satellite-based Prediction of Forage Conditions for Livestock in Northern Kenya* JSTARS2020DPN-HRA -> code for 2020 paper: Deep Prototypical Networks With Hybrid Residual Attention for Hyperspectral Image Classification* SIGNA -> code for 2022 paper: Semantic Interleaving Global Channel Attention for Multilabel Remote Sensing Image Classification* Satellite Image Classification using rmldnn and Sentinel 2 data

Segmentation

Segmentation will assign a class label to each pixel in an image. Single class models are often trained for road or building segmentation, with multi class for land use/crop type classification. Note that many articles which refer to 'hyperspectral land classification' are actually describing semantic segmentation. Note that cloud detection can be addressed with semantic segmentation and has its own section Cloud detection & removal. Read my blog post A brief introduction to satellite image segmentation with neural networks* awesome-satellite-images-segmentation* Satellite Image Segmentation: a Workflow with U-Net is a decent intro article 🔰* mmsegmentation -> Semantic Segmentation Toolbox with support for many remote sensing datasets including LoveDA, Potsdam, Vaihingen & iSAID* segmentationgym -> A neural gym for training deep learning models to carry out geoscientific image segmentation* How to create a DataBlock for Multispectral Satellite Image Semantic Segmentation using Fastai* Using a U-Net for image segmentation, blending predicted patches smoothly is a must to please the human eye -> python code to blend predicted patches smoothly. See Satellite-Image-Segmentation-with-Smooth-Blending* DCA -> code for 2022 paper: Deep Covariance Alignment for Domain Adaptive Remote Sensing Image Segmentation* SCAttNet -> Semantic Segmentation Network with Spatial and Channel Attention Mechanism* unetseg -> A set of classes and CLI tools for training a semantic segmentation model based on the U-Net architecture, using Tensorflow and Keras. This implementation is tuned specifically for satellite imagery and other geospatial raster data* Semantic Segmentation of Satellite Imagery using U-Net & fast.ai -> with repo* clusternetsegmentation -> Unsupervised Segmentation by applying K-Means clustering to the features generated by Neural Network* Collection of different Unet Variant -> demonstrates VggUnet, ResUnet, DenseUnet, Unet. AttUnet, MobileNetUnet, NestedUNet, R2AttUNet, R2UNet, SEUnet, scSEUnet, UnetXceptionResNetBlock, in keras* Efficient-Transformer -> code for 2021 paper: Efficient Transformer for Remote Sensing Image Segmentation* weaklysupervised -> code for the 2020 paper: Weakly Supervised Deep Learning for Segmentation of Remote Sensing Imagery* HRCNet-High-Resolution-Context-Extraction-Network -> code to 2021 paper: High-Resolution Context Extraction Network for Semantic Segmentation of Remote Sensing Images* Semantic segmentation of SAR images using a self supervised technique* satellite-segmentation-pytorch -> explores a wide variety of image augmentations to increase training dataset size* IEEETGRSSpectralFormer -> code for 2021 paper: Spectralformer: Rethinking hyperspectral image classification with transformers* Unsupervised Segmentation of Hyperspectral Remote Sensing Images with Superpixels -> code for 2022 paper* Semantic-Segmentation-with-Sparse-Labels -> codes and data for learning from sparse annotations* SNDF -> code for 2020 paper: Superpixel-enhanced deep neural forest for remote sensing image semantic segmentation* Satellite-Image-Classification -> using random forest or support vector machines (SVM) and sklearn* dynamic-rs-segmentation -> code for 2019 paper: Dynamic Multi-Context Segmentation of Remote Sensing Images based on Convolutional Networks* Remote-sensing-image-semantic-segmentation-tf2 -> remote sensing image semantic segmentation repository based on tf.keras includes backbone networks such as resnet, densenet, mobilenet, and segmentation networks such as deeplabv3+, pspnet, panet, and refinenet* segmentationmodels.pytorch -> Segmentation models with pretrained backbones, has been used in multiple winning solutions to remote sensing competitions* SSRN -> code for 2017 paper: Spectral-Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework* SO-DNN -> code for 2021 paper: Simplified object-based deep neural network for very high resolution remote sensing image classification* SANet -> code for 2019 paper: Scale-Aware Network for Semantic Segmentation of High-Resolution Aerial Images* aerial-segmentation -> code for 2017 paper: Learning Aerial Image Segmentation from Online Maps* IterativeSegmentation -> code for 2016 paper: Recurrent Neural Networks to Correct Satellite Image Classification Maps* Detectron2 FPN + PointRend Model for amazing Satellite Image Segmentation -> 15% increase in accuracy when compared to the U-Net model* HybridSN -> code for 2019 paper: HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification. Also a pytorch implementation here* TNNLS2022X-GPN -> code for 2022 paper: Semisupervised Cross-scale Graph Prototypical Network for Hyperspectral Image Classification* singleSceneSemSegTgrs2022 -> code for 2022 paper: Unsupervised Single-Scene Semantic Segmentation for Earth Observation* A-Fast-and-Compact-3-D-CNN-for-HSIC -> code for 2020 paper: A Fast and Compact 3-D CNN for Hyperspectral Image Classification* HSNRS -> code for 2017 paper: Hourglass-ShapeNetwork Based Semantic Segmentation for High Resolution Aerial Imagery* GiGCN -> code for 2022 paper: Graph-in-Graph Convolutional Network for Hyperspectral Image Classification* SSAN -> code for 2019 paper: Spectral-Spatial Attention Networks for Hyperspectral Image Classification* drone-images-semantic-segmentation -> Multiclass Semantic Segmentation of Aerial Drone Images Using Deep Learning* Satellite-Image-Segmentation-with-Smooth-Blending -> uses Smoothly-Blend-Image-Patches* BayesianUNet -> Pytorch Bayesian UNet model for segmentation and uncertainty prediction, applied to the Potsdam Dataset* RAANet -> code for 2022 paper: RAANet: A Residual ASPP with Attention Framework for Semantic Segmentation of High-Resolution Remote Sensing Images* wheelRutssemanticSegmentation -> code for 2022 paper: Mapping wheel-ruts from timber harvesting operations using deep learning techniques in drone imagery* LWN-for-UAVRSI -> Light-Weight Semantic Segmentation Network for UAV Remote Sensing Images, applied to Vaihingen, UAVid and UDD6 datasets* hypernet -> library which implements; accurate hyperspectral image (HSI) segmentation and analysis using deep neural networks, optimization of deep neural network architectures for hyperspectral data segmentation, hyperspectral data augmentation, validation of existent and emerging HSI segmentation algorithms, simulation of multispectral data using HSI* ST-UNet -> code for 2022 paper: Swin Transformer Embedding UNet for Remote Sensing Image Semantic Segmentation* EDFT -> code for 2022 paper: Efficient Depth Fusion Transformer for Aerial Image Semantic Segmentation* WiCoNet -> code for 2022 paper: Looking Outside the Window: Wide-Context Transformer for the Semantic Segmentation of High-Resolution Remote Sensing Images* CRGNet -> code for 2022 paper: Consistency-Regularized Region-Growing Network for Semantic Segmentation of Urban Scenes with Point-Level Annotations* SA-UNet -> code for 2022 paper: Improved U-Net Remote Sensing Classification Algorithm Fusing Attention and Multiscale Features* MANet -> code for 2020 paper: Multi-Attention-Network for Semantic Segmentation of Fine Resolution Remote Sensing Images* BANet -> code for 2021 paper: Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images* MACU-Net -> code for 2022 paper: MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images* DNAS -> code for 2022 paper: DNAS: Decoupling Neural Architecture Search for High-Resolution Remote Sensing Image Semantic Segmentation* A2-FPN -> code for 2021 paper: A2-FPN for Semantic Segmentation of Fine-Resolution Remotely Sensed Images* MAResU-Net -> code for 2020 paper: Multi-stage Attention ResU-Net for Semantic Segmentation of Fine-Resolution Remote Sensing Images* mlsegmentation -> semantic segmentation of buildings using Random Forest, Support Vector Machine (SVM) & Gradient Boosting Classifier (GBC)* RSEN -> code for 2021 paper: Robust Self-Ensembling Network for Hyperspectral Image Classification* MSNet -> code for 2022 paper: MSNet: multispectral semantic segmentation network for remote sensing images* k-textures -> code (R) for 2022 paper: K-textures, a self-supervised hard clustering deep learning algorithm for satellite image segmentation* Swin-Transformer-Semantic-Segmentation -> code for 2021 paper: Satellite Image Semantic Segmentation* UDAforRS -> code for 2022 paper: Unsupervised Domain Adaptation for Remote Sensing Semantic Segmentation with Transformer

Segmentation - Land use & land cover

U-Net for Semantic Segmentation on Unbalanced Aerial Imagery -> using the Dubai dataset 🔰
Semantic Segmentation of Dubai dataset Using a TensorFlow U-Net Model 🔰
nga-deep-learning -> performs semantic segmentation on high resultion GeoTIF data using a modified U-Net & Keras, published by NASA researchers
Automatic Detection of Landfill Using Deep Learning
SpectralNET -> a 2D wavelet CNN for Hyperspectral Image Classification, uses Salinas Scene dataset & Keras
laika -> The goal of this repo is to research potential sources of satellite image data and to implement various algorithms for satellite image segmentation
PEARL -> a human-in-the-loop AI tool to drastically reduce the time required to produce an accurate Land Use/Land Cover (LULC) map, blog post, uses Microsoft Planetary Computer and ML models run locally in the browser. Code for backelnd and frontend
Land Cover Classification with U-Net -> Satellite Image Multi-Class Semantic Segmentation Task with PyTorch Implementation of U-Net, uses DeepGlobe Land Cover Segmentation dataset, with code
Multi-class semantic segmentation of satellite images using U-Net using DSTL dataset, tensorflow 1 & python 2.7. Accompanying article
Codebase for multi class land cover classification with U-Net accompanying a masters thesis, uses Keras
dubai-satellite-imagery-segmentation -> due to the small dataset, image augmentation was used
CDL-Segmentation -> code for the 2021 paper: Deep Learning Based Land Cover and Crop Type Classification: A Comparative Study. Compares UNet, SegNet & DeepLabv3+
LoveDA -> code for the 2021 paper: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
Satellite Imagery Semantic Segmentation with CNN -> 7 different segmentation classes, DeepGlobe Land Cover Classification Challenge dataset, with repo
Aerial Semantic Segmentation using U-Net Deep Learning Model medium article, with repo
UNet-Satellite-Image-Segmentation -> A Tensorflow implentation of light UNet semantic segmentation framework
DeepGlobe Land Cover Classification Challenge solution
Semantic-segmentation-with-PyTorch-Satellite-Imagery -> predict 25 classes on RGB imagery taken to assess the damage after Hurricane Harvey
Semantic Segmentation With Sentinel-2 Imagery -> uses LandCoverNet dataset and fast.ai
CNNEnhancedGCN -> code for 2021 paper: CNN-Enhanced Graph Convolutional Network With Pixel- and Superpixel-Level Feature Fusion for Hyperspectral Image Classification
LULCMapping-WV3images-CORINE-DLMethods -> Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images
SOLC -> code for 2022 paper: MCANet: A joint semantic segmentation framework of optical and SAR images for land use classification. Uses WHU-OPT-SAR-dataset
MUnet-LUC -> Land Use with mUnet
land-cover -> code for 2021 paper: Model Generalization in Deep Learning Applications for Land Cover Mapping
generalizablersc -> code for 2022 paper: Cross-dataset Learning for Generalizable Land Use Scene Classification
Large-scale-Automatic-Identification-of-Urban-Vacant-Land -> code for 2022 paper: Large-scale automatic identification of urban vacant land using semantic segmentation of high-resolution remote sensing images
SSLTransformerRS -> code for 2022 paper: Self-supervised Vision Transformers for Land-cover Segmentation andClassification
aerial-tile-segmentation -> Large satellite image semantic segmentation into 6 classes using Tensorflow 2.0 and ISPRS benchmark dataset
LULCMapping-WV3images-CORINE-DLMethods -> code for 2022 paper: Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images
DCSA-Net -> code for 2022 paper: Dynamic Convolution Self-Attention Network for Land-Cover Classification in VHR Remote-Sensing Images
CHeGCN-CNNenhancedHeterogeneous_Graph -> code for 2022 paper: CNN-Enhanced Heterogeneous Graph Convolutional Network: Inferring Land Use from Land Cover with a Case Study of Park Segmentation

Segmentation - Vegetation, crops & crop boundaries

Сrор field boundary detection: approaches and main challenges -> Medium article, covering historical and modern approaches 🔰
kenya-crop-mask -> Annual and in-season crop mapping in Kenya - LSTM classifier to classify pixels as containing crop or not, and a multi-spectral forecaster that provides a 12 month time series given a partial input. Dataset downloaded from GEE and pytorch lightning used for training 🔰
What’s growing there? Identify crops from multi-spectral remote sensing data (Sentinel 2) using eo-learn for data pre-processing, cloud detection, NDVI calculation, image augmentation & fastai
Tree species classification from from airborne LiDAR and hyperspectral data using 3D convolutional neural networks accompanies research paper and uses fastai
crop-type-classification -> using Sentinel 1 & 2 data with a U-Net + LSTM, more features (i.e. bands) and higher resolution produced better results (article, no code)
Find sports fields using Mask R-CNN and overlay on open-street-map
An LSTM to generate a crop mask for Togo
DeepSatModels -> Code for paper "Context-self contrastive pretraining for crop type semantic segmentation"
farm-pin-crop-detection-challenge -> Using eo-learn and fastai to identify crops from multi-spectral remote sensing data
Detecting Agricultural Croplands from Sentinel-2 Satellite Imagery -> We developed UNet-Agri, a benchmark machine learning model that classifies croplands using open-access Sentinel-2 imagery at 10m spatial resolution
DeepTreeAttention -> Implementation of Hang et al. 2020 "Hyperspectral Image Classification with Attention Aided CNNs" for tree species prediction
Crop-Classification -> crop classification using multi temporal satellite images
ParcelDelineation -> using a French polygons dataset and unet in keras
crop-mask -> End-to-end workflow for generating high resolution cropland maps, uses GEE & LSTM model
DeepCropMapping -> A multi-temporal deep learning approach with improved spatial generalizability for dynamic corn and soybean mapping, uses LSTM
Segment Canopy Cover and Soil using NDVI and Rasterio
Use KMeans clustering to segment satellite imagery by land cover/land use
ResUnet-a -> Implementation of the paper "ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data" in TensorFlow
DSDpaper2020 -> The code for the paper: Crop Type Classification based on Machine Learning with Multitemporal Sentinel-1 Data
MR-DNN -> extract rice field from Landsat 8 satellite imagery
deeplearningforest_monitoring -> Estimate vegetation height, code for paper: Forest mapping and monitoring of the African continent using Sentinel-2 data and deep learning
global-cropland-mapping -> global multi-temporal cropland mapping
U-Net for Semantic Segmentation of Soyabean Crop Fields with SAR images
UNet-RemoteSensing -> uses 7 bands of Landsat and keras
Landuse_DL -> delineate landforms due to the thawing of ice-rich permafrost
canopy -> code for 2019 paper: A Convolutional Neural Network Classifier Identifies Tree Species in Mixed-Conifer Forest from Hyperspectral Imagery
RandomForest-Classification -> script is for random forest classification of remote sensing multi-band images, used in 2019 paper: Multisensor data to derive peatland vegetation communities using a fixed-wing unmanned aerial vehicle
forestchangedetection -> forest change segmentation with time-dependent models, including Siamese, UNet-LSTM, UNet-diff, UNet3D models. Code for 2021 paper: Deep Learning for Regular Change Detection in Ukrainian Forest Ecosystem With Sentinel-2
cultionet -> segmentation of cultivated land, built on PyTorch Geometric and PyTorch Lightning
sentinel-tree-cover -> code for 2020 paper: A global method to identify trees outside of closed-canopy forests with medium-resolution satellite imagery
crop-type-detection-ICLR-2020 -> Winning Solutions from Crop Type Detection Competition at CV4A workshop, ICLR 2020
Crop identification using satellite imagery -> Medium article, introduction to crop identification
S4A-Models -> Various experiments on the Sen4AgriNet dataset
attention-mechanism-unet -> code for 2022 paper: An attention-based U-Net for detecting deforestation within satellite sensor imagery
Cocoaplantationsdetection -> Detecting cocoa plantation in Ivory Coast using Sentinel-2 remote sensing data using KNN, SVM, Random Forest and MLP

Segmentation - Water, coastlines & floods

Flood Detection and Analysis using UNET with Resnet-34 as the back bone uses fastai 🔰
Automatic Flood Detection from Satellite Images Using Deep Learning 🔰
UNSOAT used fastai to train a Unet to perform semantic segmentation on satellite imageries to detect water - paper + notebook, accuracy 0.97, precision 0.91, recall 0.92
Semi-Supervised Classification and Segmentation on High Resolution Aerial Images - Solving the FloodNet problem
Houston_flooding -> labeling each pixel as either flooded or not using data from Hurricane Harvey. Dataset consisted of pre and post flood images, and a ground truth floodwater mask was created using unsupervised clustering (with DBScan) of image pixels with human cluster verification/adjustment
ml4floods -> An ecosystem of data, models and code pipelines to tackle flooding with ML
A comprehensive guide to getting started with the ETCI Flood Detection competition -> using Sentinel1 SAR & pytorch
Map Floodwater of SAR Imagery with SageMaker -> applied to Sentinel-1 dataset
1st place solution for STAC Overflow: Map Floodwater from Radar Imagery hosted by Microsoft AI for Earth -> combines Unet with Catboostclassifier, taking their maxima, not the average
hydra-floods -> an open source Python application for downloading, processing, and delivering surface water maps derived from remote sensing data
CoastSat -> tool for mapping coastlines which has an extension CoastSeg using segmentation models
SatelliteFloodSegmentationofHarvey -> explores both deep learning and traditional kmeans
Flood Event Detection Utilizing Satellite Images
ETCI-2021-Competition-on-Flood-Detection -> Experiments on Flood Segmentation on Sentinel-1 SAR Imagery with Cyclical Pseudo Labeling and Noisy Student Training, with arxiv paper
FDSI -> Flood Detection in Satellite Images - 2017 Multimedia Satellite Task
deepwatermap -> a deep model that segments water on multispectral images
rivamap -> an automated river analysis and mapping engine
deep-water -> track changes in water level
WatNet -> A deep ConvNet for surface water mapping based on Sentinel-2 image, uses the Earth Surface Water Dataset
A-U-Net-for-Flood-Extent-Mapping -> in keras
floatingobjects -> code for the paper: TOWARDS DETECTING FLOATING OBJECTS ON A GLOBAL SCALE WITHLEARNED SPATIAL FEATURES USING SENTINEL 2. Uses U-Net & pytorch
River-Network-Extraction-from-Satellite-Image-using-UNet-and-Tensorflow -> uses Sentinel-2 imagery
SpaceNet8 -> baseline Unet solution to detect flooded roads and buildings
dlsim -> code for 2020 paper: Breaking the Limits of Remote Sensing by Simulation and Deep Learning for Flood and Debris Flow Mapping
Water-HRNet -> HRNet trained on Sentinel 2
semantic segmentation model to identify newly developed or flooded land using NAIP imagery provided by the Chesapeake Conservancy, training on MS Azure

Segmentation - Fire, smoke & burn areas

SatelliteVu-AWS-Disaster-Response-Hackathon -> fire spread prediction using classical ML & deep learning 🔰
Wild Fire Detection using U-Net trained on Databricks & Keras, semantic segmentation
A Practical Method for High-Resolution Burned Area Monitoring Using Sentinel-2 and VIIRS with code. Dataset created on Google Earth Engine, downloaded to local machine for model training using fastai. The BA-Net model used is much smaller than U-Net, resulting in lower memory requirements and a faster computation
AI Geospatial Wildfire Risk Prediction -> A predictive model using geospatial raster data to asses wildfire hazard potential over the contiguous United States using Unet
IndustrialSmokePlumeDetection -> using Sentinel-2 & a modified ResNet-50
burned-area-detection -> uses Sentinel-2
rescue -> code of the paper: Attention to fires: multi-channel deep-learning models forwildfire severity prediction
smoke_segmentation -> Segmenting smoke plumes and predicting density from GOES imagery
wildfire-detection -> Using Vision Transformers for enhanced wildfire detection in satellite images
BurnedAreaDetection -> Detecting Burned Areas with Sentinel-2 data

Segmentation - Landslides

landslide4sense -> a competition focused on landslide detection using globally distributed multi-source satellite imagery. Baseline solution unet 🔰
landslide-mapping-with-cnn -> code for 2021 paper: A new strategy to map landslides with a generalized convolutional neural network
RelictlandslidesCNN_kmeans -> code for 2022 paper: Relict landslide detection in rainforest areas using a combination of k-means clustering algorithm and Deep-Learning semantic segmentation models
Landslide-mapping-on-SAR-data-by-Attention-U-Net -> code for 2022 paper: Rapid Mapping of landslide on SAR data by Attention U-net

Segmentation - Glaciers

HED-UNet -> a model for simultaneous semantic segmentation and edge detection, examples provided are glacier fronts and building footprints using the Inria Aerial Image Labeling dataset
glacier_mapping -> Mapping glaciers in the Hindu Kush Himalaya, Landsat 7 images, Shapefile labels of the glaciers, Unet with dropout
glacier-detect-ML -> a simple logistic regression model to identify a glacier in Landsat satellite imagery
GlacierSemanticSegmentation -> uses unet
Antarctic-fracture-detection -> uses UNet with the MODIS Mosaic of Antarctica to detect surface fractures (paper)

Segmentation - Other environmental

Detection of Open Landfills -> uses Sentinel-2 to detect large changes in the Normalized Burn Ratio (NBR)
seaiceremote_sensing -> Sea Ice Concentration classification
Methane-detection-from-hyperspectral-imagery -> code for 2020 paper: Deep Remote Sensing Methods for Methane Detection in Overhead Hyperspectral Imagery
EddyNet -> A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies
schisto-vegetation -> code for 2022 paper: Deep Learning Segmentation of Satellite Imagery Identifies Aquatic Vegetation Associated with Snail Intermediate Hosts of Schistosomiasis in Senegal, Africa

Segmentation - Roads

Extracting roads is challenging due to the occlusions caused by other objects and the complex traffic environment* Road detection using semantic segmentation and albumentations for data augmention using the Massachusetts Roads Dataset, U-net & Keras. With code 🔰* MLEPFLProject2 -> U-Net in Pytorch to perform semantic segmentation of roads on satellite images 🔰* Semantic segmentation of roads and highways using Sentinel-2 imagery (10m) super-resolved using the SENX4 model up to x4 the initial spatial resolution (2.5m) (results, no repo)* Semantic Segmentation of roads using U-net Keras, OSM data, project summary article by student, no code* Winning Solutions from SpaceNet Road Detection and Routing Challenge* RoadVecNet -> Road-Network-Segmentation-and-Vectorization in keras with dataset and paper* Detecting road and road types jupyter notebook* awesome-deep-map -> A curated list of resources dedicated to deep learning / computer vision algorithms for mapping. The mapping problems include road network inference, building footprint extraction, etc.* RoadTracer: Automatic Extraction of Road Networks from Aerial Images -> uses an iterative search process guided by a CNN-based decision function to derive the road network graph directly from the output of the CNN* roaddetectionmtl -> Road Detection using a multi-task Learning technique to improve the performance of the road detection task by incorporating prior knowledge constraints, uses the SpaceNet Roads Dataset* roadconnectivity -> Improved Road Connectivity by Joint Learning of Orientation and Segmentation (CVPR2019)* Road-Network-Extraction using classical Image processing -> blur & canny edge detection* SPINRoadMapper -> Extracting Roads from Aerial Images via Spatial and Interaction Space Graph Reasoning for Autonomous Driving* roadextractionremotesensing -> pytorch implementation, CVPR2018 DeepGlobe Road Extraction Challenge submission. See also DeepGlobe-Road-Extraction-Challenge* RoadDetections dataset by Microsoft* CoANet -> Connectivity Attention Network for Road Extraction From Satellite Imagery. The CoA module incorporates graphical information to ensure the connectivity of roads are better preserved. With paper* Satellite Imagery Road Segmentation -> intro articule on Medium using the kaggle Massachusetts Roads Dataset* Label-Pixels -> for semantic segmentation of roads and other features* Satellite-image-road-extraction -> code for 2018 paper: Road Extraction by Deep Residual U-Net* roadbuildingextraction -> Pytorch implementation of U-Net architecture for road and building extraction* Satellite-Imagery-Road-Extraction -> research project in keras* SGCN -> code for 2021 paper: Split Depth-Wise Separable Graph-Convolution Network for Road Extraction in Complex Environments From High-Resolution Remote-Sensing Images* ASPN -> code for 2020 paper: Road Segmentation for Remote Sensing Images using Adversarial Spatial Pyramid Networks* FCNs-for-road-extraction-keras -> Road extraction of high-resolution remote sensing images based on various semantic segmentation networks* cresi -> Road network extraction from satellite imagery, with speed and travel time estimates* road-extraction-d-linknet -> code for 2018 paper: D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction* Sat2Graph -> code for 2020 paper: Road Graph Extraction through Graph-Tensor Encoding* Image-Segmentation) -> using Massachusetts Road dataset and fast.ai* RoadTracer-M -> code for 2019 paper: Road Network Extraction from Satellite Images Using CNN Based Segmentation and Tracing* ScRoadExtractor -> code for 2020 paper: Scribble-based Weakly Supervised Deep Learning for Road Surface Extraction from Remote Sensing Images* RoadDA -> code for 2021 paper: Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training for Road Segmentation of Remote Sensing Images* DeepSegmentor -> A Pytorch implementation of DeepCrack and RoadNet projects* CascadeResidualAttentionEnhancedforRefinementRoadExtraction -> code for 2021 paper: Cascaded Residual Attention Enhanced Road Extraction from Remote Sensing Images* nia-road-baseline -> code for 2020 paper: NL-LinkNet: Toward Lighter but More Accurate Road Extraction with Non-Local Operations* IRSR-net -> code for 2022 paper: Lightweight Remote Sensing Road Detection Network* hironex -> A python tool for automatic, fully unsupervised extraction of historical road networks from historical maps* Roaddetectionmodel -> code for 2022 paper: Mapping Roads in the Brazilian Amazon with Artificial Intelligence and Sentinel-2* DTnet -> code for 2022 paper: Road detection via a dual-task network based on cross-layer graph fusion modules* Automatic-Road-Extraction-from-Historical-Maps-using-Deep-Learning-Techniques -> code for the paper: Automatic Road Extraction from Historical Maps using Deep Learning Techniques: A Regional Case Study of Turkey in a German World War II map* IstanbulDataset -> segmentation on the Istanbul, Inria and Massachusetts datasets

Segmentation - Buildings & rooftops

Road and Building Semantic Segmentation in Satellite Imagery uses U-Net on the Massachusetts Roads Dataset & keras 🔰
find-unauthorized-constructions-using-aerial-photography -> semantic segmentation using U-Net with custom_f1 metric & Keras. The creation of the dataset is described in this article 🔰
Semantic Segmentation on Aerial Images using fastai uses U-Net on the Inria Aerial Image Labeling Dataset of urban settlements in Europe and the United States, and is labelled as a building and not building classes (no repo) 🔰
Building footprint detection with fastai on the challenging SpaceNet7 dataset uses U-Net & fastai 🔰
Pix2Pix-for-Semantic-Segmentation-of-Satellite-Images -> using Pix2Pix GAN network to segment the building footprint from Satellite Images, uses tensorflow
SpaceNetUnet -> Baseline model is U-net like, applied to SpaceNet Vegas data, using Keras
automated-building-detection -> Input: very-high-resolution (<= 0.5 m/pixel) RGB satellite images. Output: buildings in vector format (geojson), to be used in digital map products. Built on top of robosat and robosat.pink.
projectsunroofindia -> Analyzed Google Satellite images to generate a report on individual house rooftop's solar power potential, uses a range of classical computer vision techniques (e.g Canny Edge Detection) to segment the roofs
JointNet-A-Common-Neural-Network-for-Road-and-Building-Extraction
Mapping Africa’s Buildings with Satellite Imagery: Google AI blog post. See the open-buildings dataset
nz_convnet -> A U-net based ConvNet for New Zealand imagery to classify building outlines
polycnn -> End-to-End Learning of Polygons for Remote Sensing Image Classification
spacenetbuildingdetection solution by motokimura using Unet
How to extract building footprints from satellite images using deep learning
Vec2Instance -> applied to the SpaceNet challenge AOI 2 (Vegas) building footprint dataset, tensorflow v1.12
EarthquakeDamageDetection -> Buildings segmentation from satellite imagery and damage classification for each build, using Keras
Semantic-segmentation repo by fuweifu-vtoo -> uses pytorch and the Massachusetts Buildings & Roads Datasets
Extracting buildings and roads from AWS Open Data using Amazon SageMaker -> uses merged RGB (SpaceNet) and LiDAR (USGS 3DEP) datasets with Unet to reproduce the winning algorithm from SpaceNet challenge 4 by XD_XD. With repo
TF-SegNet -> AirNet is a segmentation network based on SegNet, but with some modifications
rgb-footprint-extract -> a Semantic Segmentation Network for Urban-Scale Building Footprint Extraction Using RGB Satellite Imagery, DeepLavV3+ module with a Dilated ResNet C42 backbone
SpaceNetExploration -> A sample project demonstrating how to extract building footprints from satellite images using a semantic segmentation model. Data from the SpaceNet Challenge
Rooftop-Instance-Segmentation -> VGG-16, Instance Segmentation, uses the Airs dataset
solar-farms-mapping -> An Artificial Intelligence Dataset for Solar Energy Locations in India
poultry-cafos -> This repo contains code for detecting poultry barns from high-resolution aerial imagery and an accompanying dataset of predicted barns over the United States
ssai-cnn -> This is an implementation of Volodymyr Mnih's dissertation methods on his Massachusetts road & building dataset
Remote-sensing-building-extraction-to-3D-model-using-Paddle-and-Grasshopper
segmentation-enhanced-resunet -> Urban building extraction in Daejeon region using Modified Residual U-Net (Modified ResUnet) and applying post-processing
Mask RCNN for Spacenet Off Nadir Building Detection
GRSLBFEMA -> Deep Learning-based Building Footprint Extraction with Missing Annotations using a novel loss function
FER-CNN -> Detection, Classification and Boundary Regularization of Buildings in Satellite Imagery Using Faster Edge Region Convolutional Neural Networks, with paper
UNET-Image-Segmentation-Satellite-Picture -> Unet to predict roof tops on Crowed AI Mapping dataset, uses keras
Vector-Map-Generation-from-Aerial-Imagery-using-Deep-Learning-GeoSpatial-UNET -> applied to geo-referenced images which are very large size > 10k x 10k pixels
building-footprint-segmentation -> pip installable library to train building footprint segmentation on satellite and aerial imagery, applied to Massachusetts Buildings Dataset and Inria Aerial Image Labeling Dataset
SemSegBuildings -> Project using fast.ai framework for semantic segmentation on Inria building segmentation dataset
FCNN-example -> overfit to a given single image to detect houses
SAT2LOD2 -> an open-source, python-based GUI-enabled software that takes the satellite images as inputs and returns LoD2 building models as outputs, with paper
SatFootprint -> building segmentation on the Spacenet 7 dataset
Building-Detection -> code for running a Raster Vision experiment to train a model to detect buildings from satellite imagery in three cities in Latin America
Multi-building-tracker -> code for paper: Multi-target building tracker for satellite images using deep learning
Boundary Enhancement Semantic Segmentation for Building Extraction
UNetkerasfor_RSimage -> keras code for binary semantic segmentation
Spacenet-Building-Detection -> uses keras
LGPNet-BCD -> code for 2021 paper: Building Change Detection for VHR Remote Sensing Images via Local-Global Pyramid Network and Cross-Task Transfer Learning Strategy
MTLhomoscedasticSRB -> code for 2021 paper: A Multi-Task Deep Learning Framework for Building Footprint Segmentation
UNet_CNN -> UNet model to segment building coverage in Boston using Remote sensing data, uses keras
FDANet -> code for 2021 paper: Full-Level Domain Adaptation for Building Extraction in Very-High-Resolution Optical Remote-Sensing Images
CBRNet -> code for 2022 paper: A Coarse-to-fine Boundary Refinement Network for Building Extraction from Remote Sensing Imagery
ASLNet -> code for 2021 paper: Adversarial Shape Learning for Building Extraction in VHR Remote Sensing Images
BRRNet -> implementation of Modified U-Net from 2020 paper: BRRNet: A Fully Convolutional Neural Network for Automatic Building Extraction From High-Resolution Remote Sensing Images
Multi-Scale-Filtering-Building-Index -> Python implementation of building extraction index proposed in 2019 paper: A Multi - Scale Filtering Building Index for Building Extraction in Very High - Resolution Satellite Imagery
Models for Remote Sensing -> long list of unets etc applied to building detection
boundarylossforremotesensing -> code for 2019 paper: Boundary Loss for Remote Sensing ImagerySemantic Segmentation
Open Cities AI Challenge -> Segmenting Buildings for Disaster Resilience. Winning solutions on Github
MAPNet -> code for 2020 paper: Multi Attending Path Neural Network for Building Footprint Extraction from Remote Sensed Imagery
dual-hrnet -> localizing buildings and classifying their damage level
ESFNet -> code for 2019 paper: Efficient Network for Building Extraction from High-Resolution Aerial Images
rooftop-detection-python -> Detect Rooftops from low resolution satellite images and calculate area for cultivation and solar panel installment using classical computer vision techniques
kerassegmentationmodels -> code for 2022 paper: Using Open Vector-Based Spatial Data to Create Semantic Datasets for Building Segmentation for Raster Data
CVCMFFNet -> code for 2021 paper: Complex-Valued Convolutional and Multifeature Fusion Network for Building Semantic Segmentation of InSAR Images
STEB-UNet -> code for 2022 paper: A Swin Transformer-Based Encoding Booster Integrated in U-Shaped Network for Building Extraction
dfc2020_baseline -> Baseline solution for the IEEE GRSS Data Fusion Contest 2020. Predict land cover labels from Sentinel-1 and Sentinel-2 imagery. Code for 2020 paper: Weakly Supervised Semantic Segmentation of Satellite Images for Land Cover Mapping
Fusing multiple segmentation models based on different datasets into a single edge-deployable model -> roof, car & road segmentation
ground-truth-gan-segmentation -> use Pix2Pix to segment the footprint of a building. The dataset used is AIRS
UNICEF-Giga_Sudan -> Detecting school lots from satellite imagery in Southern Sudan using a UNET segmentation model
buildingfootprintextraction -> The project retrieves satellite imagery from Google and performs building footprint extraction using a U-Net.
projectRegularization -> code for 2019 paper: Regularization of building boundaries in satellite images using adversarial and regularized losses
PolyWorldPretrainedNetwork -> code for 2021 paper: Polygonal Building Extraction with Graph Neural Networks in Satellite Images
dlimagesegmentation -> code for 2022 paper: Uncertainty-Aware Interpretable Deep Learning for Slum Mapping and Monitoring. Uses SHAP
UBC-dataset -> a dataset for building detection and classification from very high-resolution satellite imagery with the focus on object-level interpretation of individual buildings
GeoSeg -> code for 2022 paper: UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery
BESNet -> code for 2022 paper: BES-Net: Boundary Enhancing Semantic Context Network for High-Resolution Image Semantic Segmentation. Applied to Vaihingen and Potsdam datasets
CVNet -> code for 2022 paper: CVNet: Contour Vibration Network for Building Extraction
CFENet -> code for 2022 paper: A Context Feature Enhancement Network for Building Extraction from High-Resolution Remote Sensing Imagery
HiSup -> code for 2022 paper: Accurate Polygonal Mapping of Buildings in Satellite Imagery
BuildingExtraction -> code for 2021 paper: Building Extraction from Remote Sensing Images with Sparse Token Transformers
coseg_building -> code for the 2022 paper: CrossGeoNet: A Framework for Building Footprint Generation of Label-Scarce Geographical Regions
AFM_building -> code for 2021 paper: Building Footprint Generation Through Convolutional Neural Networks With Attraction Field Representation
ramp-code -> repository for the ramp (Replicable AI for MicroPlanning) project
Building-instance-segmentation -> code for 2022 paper: Multi-Modal Feature Fusion Network with Adaptive Center Point Detector for Building Instance Extraction
CGSANet -> code for the 2021 paper: CGSANet: A Contour-Guided and Local Structure-Aware Encoder–Decoder Network for Accurate Building Extraction From Very High-Resolution Remote Sensing Imagery

Segmentation - Solar panels

DeepSolar -> A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States. Dataset on kaggle, actually used a CNN for classification and segmentation is obtained by applying a threshold to the activation map. Original code is tf1 but tf2/kers and a pytorch implementation are available. Also checkout Visualizations and in-depth analysis .. of the factors that can explain the adoption of solar energy in .. Virginia and DeepSolar tracker: towards unsupervised assessment with open-source data of the accuracy of deep learning-based distributed PV mapping
hyperionsolarnet -> trained classificaton & segmentation models on RGB imagery from Google Maps. Provides app for viewing predictions, and has arxiv paper and a nice webpage
3D-PV-Locator -> Large-scale detection of rooftop-mounted photovoltaic systems in 3D
PV_Pipeline -> PyTorch models and pipeline developed for "DeepSolar for Germany"
solar-panels-detection -> using SegNet, Fast SCNN & ResNet
predictpvyield -> Using optical flow & machine learning to predict PV yield
Large-scale-solar-plant-monitoring -> code for the paper "Remote Sensing for Monitoring of Photovoltaic Power Plants in Brazil Using Deep Semantic Segmentation"
Panel-Segmentation -> Determine the presence of a solar array in the satellite image (boolean True/False), using a VGG16 classification model
Roofpedia -> an open registry of green roofs and solar roofs across the globe identified by Roofpedia through deep learning
Predicting the Solar Potential of Rooftops using Image Segmentation and Structured Data Medium article, using 20cm imagery & Unet
solar-pv-global-inventory -> code from the Nature paper of Kruitwagen et al, used to produce a global inventory of utility-scale solar photvoltaic generating stations
remote-sensing-solar-pv -> A repository for sharing progress on the automated detection of solar PV arrays in sentinel-2 remote sensing imagery
solar-panel-segmentation) -> Finding solar panels using USGS satellite imagery
solar_seg -> Solar segmentation of PV modules (sub elements of panels) using drone images and fast.ai
solarplantdetection -> boundary extraction of Photovoltaic (PV) plants using Mask RCNN and Amir dataset
SolarDetection -> unet on satellite image from the USA and France
adopptrs -> Automatic Detection Of Photovoltaic Panels Through Remote Sensing using unet & pytorch
solar-panel-locator -> the number of solar panel pixels was only ~0.2% of the total pixels in the dataset, so solar panel data was upsampled to account for the class imbalance
projects-solar-panel-detection -> List of project to detect solar panels from aerial/satellite images
Satellite_ComputerVision -> UNET to detect solar arrays from Sentinel-2 data, using Google Earth Engine and Tensorflow. Also covers parking lot detection
oodinstancessophia -> Material for the poster "The effect of PV array instances and images backgrounds on OOD generalization"
photovoltaic-detection -> Detecting available rooftop area from satellite images to install photovoltaic panels

Segmentation - Other manmade

Aarsh2001/MLChallengeNRSC -> Electrical Substation detection
electricalsubstationdetection -> using UNet, Albumentations for image augmentation, and OpenCV for computer vision tasks
PLGAN-for-Power-Line-Segmentation -> code for 2022 paper: PLGAN: Generative Adversarial Networks for Power-Line Segmentation in Aerial Images

Instance segmentation

In instance segmentation, each individual 'instance' of a segmented area is given a unique lable. For detection of very small objects this may a good approach, but it can struggle seperating individual objects that are closely spaced.* MaskRCNN generates bounding boxes and segmentation masks for each instance of an object in the image. It is very commonly used for instance segmentation & object detection* Instance segmentation of center pivot irrigation system in Brazil using free Landsat images, mask R-CNN & Keras 🔰* Building-Detection-MaskRCNN -> Building detection from the SpaceNet dataset by using Mask RCNN 🔰* Oil tank instance segmentation with Mask R-CNN with accompanying article using Keras & Airbus Oil Storage Detection Dataset on Kaggle* MaskRCNN-for-Caravans -> detect caravan footprints from OS imagery* parkingbaysdetectron2 -> Detecting parking bays with satellite imagery. Used Detectron2 and synthetic data with Unreal, superior performance to using Mask RCNN* Locate buildings with a dark roof that feed heat island phenomenon using Mask RCNN -> with repo, used INRIA dataset & labelme for annotation* CircleFinder -> Circular Shapes Detection in Satellite Imagery, 2nd place solution to the Circle Finder Challenge* LawnmaskRCNN -> Detecting lawns from satellite images of properties in the Cedar Rapids area using Mask-R-CNN* CropMaskRCNN -> Segmenting center pivot agriculture to monitor crop water use in drylands with Mask R-CNN and Landsat satellite imagery* Mask RCNN for Spacenet Off Nadir Building Detection* CATNet -> code for 2021 paper: Learning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing Images* Object-Detection-on-Satellite-Images-using-Mask-R-CNN -> detect ships* FactSeg -> Foreground Activation Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery (TGRS), also see FarSeg and FreeNet, implementations of research paper* aquapython -> detecting aquaculture farms using Mask R-CNN

Panoptic segmentation

Things and stuff or how remote sensing could benefit from panoptic segmentation
Panoptic Segmentation Meets Remote Sensing (paper)
pastis-benchmark
Panoptic-Generator -> This module converts GIS data into panoptic segmentation tiles
BSB-Aerial-Dataset -> an example on how to use Detectron2's Panoptic-FPN in the BSB Aerial Dataset
utae-paps -> PyTorch implementation of U-TAE and PaPs for satellite image time series panoptic segmentation

Object detection

Several different techniques can be used to count the number of objects in an image. The returned data can be an object count (regression), a bounding box around individual objects in an image (typically using Yolo or Faster R-CNN architectures), a pixel mask for each object (instance segmentation), key points for an an object (such as wing tips, nose and tail of an aircraft), or simply a classification for a sliding tile over an image. A good introduction to the challenge of performing object detection on aerial imagery is given in this paper. In summary, images are large and objects may comprise only a few pixels, easily confused with random features in background. For the same reason, object detection datasets are inherently imbalanced, since the area of background typically dominates over the area of the objects to be detected. In general object detection performs well on large objects, and gets increasingly difficult as the objects get smaller & more densely packed. Model accuracy falls off rapidly as image resolution degrades, so it is common for object detection to use very high resolution imagery, e.g. 30cm RGB. A particular characteristic of aerial images is that objects can be oriented in any direction, so using rotated bounding boxes which aligning with the object can be crucial for extracting metrics such as the length and width of an object. Note that newer models such as Yolov5 may not achieve the same performance as 'older' models like Faster RCNN or Retinanet since they are no longer pre-trained on datasets such as ImageNet, but smaller datasets such as COCO dataset.* Object detection on Satellite Imagery using RetinaNet -> using the Kaggle Swimming Pool and Car Detection dataset 🔰* Tackling the Small Object Problem in Object Detection 🔰* Background reading: Anchor Boxes for Object Detection* Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review* awesome-aerial-object-detection bu murari023, another by visionxiang and awesome-tiny-object-detection list many relevant papers* Object Detection Accuracy as a Function of Image Resolution -> Medium article using COWC dataset, performance rapidly degrades below 30cm imagery* Satellite Imagery Multiscale Rapid Detection with Windowed Networks (SIMRDWN) -> combines some of the leading object detection algorithms into a unified framework designed to detect objects both large and small in overhead imagery. Train models and test on arbitrary image sizes with YOLO (versions 2 and 3), Faster R-CNN, SSD, or R-FCN.* YOLTv4 -> YOLTv4 is designed to detect objects in aerial or satellite imagery in arbitrarily large images that far exceed the ~600×600 pixel size typically ingested by deep learning object detection frameworks. Read Announcing YOLTv4: Improved Satellite Imagery Object Detection* Tensorflow Benchmarks for Object Detection in Aerial Images -> tensorflow-based codebase created to build benchmarks for object detection in aerial images* Pytorch Benchmarks for Object Detection in Aerial Images -> pytorch-based codebase created to build benchmarks for object detection in aerial images using mmdetection* ASPDNet -> Counting dense objects in remote sensing images, arxiv paper* xview-yolov3 -> xView 2018 Object Detection Challenge: YOLOv3 Training and Inference* Faster RCNN for xView satellite data challenge* How to detect small objects in (very) large images -> A practical guide to using Slicing-Aided Hyper Inference (SAHI) for performing inference on the DOTAv1.0 object detection dataset using the mmdetection framework* Object Detection Satellite Imagery Multi-vehicles Dataset (SIMD) -> RetinaNet,Yolov3 and Faster RCNN for multi object detection on satellite images dataset* SNIPER/AutoFocus -> an efficient multi-scale object detection training/inference algorithm* marinedebrisML -> Marine debris detection, uses 3-meter imagery product called Planetscope with bands in the red, green, blue, and near-infrared. Uses Tensorflow Object Detection API with pre-trained resnet 101* pool-detection-from-aerial-imagery -> Use Icevision and Detectron2 to detect swimming pools from aerial imagery* Electric-Pylon-Detection-in-RSI -> a dataset which contains 1500 remote sensing images of electric pylons used to train ten deep learning models* Synthesizing Robustness YOLTv4 Results Part 2: Dataset Size Requirements and Geographic Insights -> quantify how much harder rare objects are to localize* IS-Count -> IS-Count is a sampling-based and learnable method for estimating the total object count in a region. * Object Detection On Aerial Imagery Using RetinaNet* Clustered-Object-Detection-in-Aerial-Image* yolov5sforsatelliteimagery -> yolov5s applied to the DOTA dataset* RetinaNet-PyTorch -> RetinaNet implementation on remote sensing ship dataset (SSDD)* Detecting-Cyclone-Centers-Custom-YOLOv3 -> tropical cyclones (TCs) are intense warm-corded cyclonic vortices, developed from low-pressure systems over the tropical oceans and driven by complex air-sea interaction* Object-Detection-YoloV3-RetinaNet-FasterRCNN -> trained on a private datset* Google-earth-Object-Recognition -> Code for training and evaluating on Dior Dataset (Google Earth Images) using RetinaNet and YOLOV5* HIECTOR: Hierarchical object detector at scale -> HIECTOR facilitates multiple satellite data collections of increasingly detailed spatial resolution for a cost-efficient and accurate object detection over large areas* Detection of Multiclass Objects in Optical Remote Sensing Images -> code for 2018 paper: Detection of Multiclass Objects in Optical Remote Sensing Images* SB-MSN -> Sampling-Balance based Multi-stage Network (SB-MSN) for aerial image object detection. Code for 2021 paper: Improving Training Instance Quality in Aerial Image Object Detection With a Sampling-Balance-Based Multistage Network* yoltv5 -> detects objects in arbitrarily large aerial or satellite images that far exceed the ~600×600 pixel size typically ingested by deep learning object detection frameworks. Uses YOLOv5 & pytorch* AIR -> A deep learning object detector framework written in Python for supporting Land Search and Rescue Missions* diordetect -> benchmarks for object detection on DIOR dataset* Panchromatic to Multispectral: Object Detection Performance as a Function of Imaging Bands -> Medium article, concludes that more bands are not always beneficial, but likely varies by use case* OPLD-Pytorch -> code for 2020 paper: Learning Point-Guided Localization for Detection in Remote Sensing Images* F3Net -> code for 2020 paper: Feature Fusion and Filtration Network for Object Detection in Optical Remote Sensing Images* GLNet -> code for 2021 paper: Global to Local: Clip-LSTM-Based Object Detection From Remote Sensing Images* SRAF-Net -> code for 2021 paper: A Scene-Relevant Anchor-Free Object Detection Network in Remote Sensing Images* objectdetectioninremotesensing_images -> using CNN and attention mechanism* SHAPObjectDetection -> code for 2022 paper: SHAP-Based Interpretable Object Detection Method for Satellite Imagery* NWD -> code for 2021 paper: A Normalized Gaussian Wasserstein Distance for Tiny Object Detection. Uses AI-TOD dataset* MSFC-Net -> code for 2021 paper: Multiscale Semantic Fusion-Guided Fractal Convolutional Object Detection Network for Optical Remote Sensing Imagery* LO-Det -> code for the 2021 paper: LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images* R2IPoints -> code for 2022 paper: R²IPoints: Pursuing Rotation-Insensitive Point Representation for Aerial Object Detection* Object-Detection -> code for 2022 paper: Multi-Scale Object Detection with the Pixel Attention Mechanism in a Complex Background* mmdet-rfla -> code for 2022 paper: RFLA: Gaussian Receptive based Label Assignment for Tiny Object Detection* Interactive-Multi-Class-Tiny-Object-Detection -> code for 2022 paper: Interactive Multi-Class Tiny-Object Detection* small-object-detection-benchmark -> code for ICIP 2022 paper: Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection

Object counting

When the object count, but not its shape is required, U-net can be used to treat this as an image-to-image translation problem.* centroid-unet -> Centroid-UNet is deep neural network model to detect centroids from satellite images, with paper 🔰* cownter_strike -> counting cows, located with point-annotations, two models: CSRNet (a density-based method) & LCFCN (a detection-based method)* DO-U-Net -> an effective approach for when the size of an object needs to be known, as well as the number of objects in the image, initially created to segment and count Internally Displaced People (IDP) camps in Afghanistan* Cassava Crop Counting* Counting from Sky -> A Large-scale Dataset for Remote Sensing Object Counting and A Benchmark Method* PSGCNet -> code for 2022 paper: PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote Sensing Images* psgcnet -> code for 2022 paper: PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote-Sensing Images

Object detection with rotated bounding boxes

Orinted bounding boxes (OBB) are polygons representing rotated rectangles* For datasets checkout DOTA & HRSC2016* mmrotate -> Rotated Object Detection Benchmark, with pretrained models and function for inferencing on very large images* OBBDetection -> an oriented object detection library, which is based on MMdetection* rotate-yolov3 -> Rotation object detection implemented with yolov3. Also see yolov3-polygon* DRBox -> for detection tasks where the objects are orientated arbitrarily, e.g. vehicles, ships and airplanes* s2anet -> Official code of the paper 'Align Deep Features for Oriented Object Detection'* CFC-Net -> Official implementation of "CFC-Net: A Critical Feature Capturing Network for Arbitrary-Oriented Object Detection in Remote Sensing Images"* ReDet -> Official code of the paper "ReDet: A Rotation-equivariant Detector for Aerial Object Detection"* BBAVectors-Oriented-Object-Detection -> Oriented Object Detection in Aerial Images with Box Boundary-Aware Vectors* CSLRetinaNetTensorflow -> Code for ECCV 2020 paper: Arbitrary-Oriented Object Detection with Circular Smooth Label* r3det-on-mmdetection -> R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object* R-DFPNFPNTensorflow -> Rotation Dense Feature Pyramid Networks (Tensorflow)* R2CNNFaster-RCNNTensorflow -> Rotational region detection based on Faster-RCNN* Rotated-RetinaNet -> implemented in pytorch, it supports the following datasets: DOTA, HRSC2016, ICDAR2013, ICDAR2015, UCAS-AOD, NWPU VHR-10, VOC2007* OBBDetSwin -> The sixth place winning solution in 2021 Gaofen Challenge* CG-Net -> Learning Calibrated-Guidance for Object Detection in Aerial Images. With paper* OrientedRepPointsDOTA -> Oriented RepPoints + Swin Transformer/ReResNet* yolov5obb -> yolov5 + Oriented Object Detection* How to Train YOLOv5 OBB -> YOLOv5 OBB tutorial and YOLOv5 OBB noteboook* OHDetTensorflow -> can be applied to rotation detection and object heading detection* Seodore -> framework maintaining recent updates of mmdetection* Rotation-RetinaNet-PyTorch -> oriented detector Rotation-RetinaNet implementation on Optical and SAR ship dataset* AIDet -> an open source object detection in aerial image toolbox based on MMDetection* rotation-yolov5 -> rotation detection based on yolov5* ShipDetection -> Ship Detection in HR Optical Remote Sensing Images via Rotated Bounding Box, based on Faster R-CNN and ORN, uses caffe* SLRDet -> project based on mmdetection to reimplement RRPN and use the model Faster R-CNN OBB* AxisLearning -> code for 2020 paper: Axis Learning for Orientated Objects Detection in Aerial Images* DetectionandRecognitioninRemoteSensingImage -> This work uses PaNet to realize Detection and Recognition in Remote Sensing Image by MXNet* DrBox-v2-tensorflow -> tensorflow implementation of DrBox-v2 which is an improved detector with rotatable boxes for target detection in remote sensing images* Rotation-EfficientDet-D0 -> A PyTorch Implementation Rotation Detector based EfficientDet Detector, applied to custom rotation vehicle datasets* DODet -> Dual alignment for oriented object detection, uses DOTA dataset. With paper* GF-CSL -> code for 2022 paper: Gaussian Focal Loss: Learning Distribution Polarized Angle Prediction for Rotated Object Detection in Aerial Images* simplifiedrboxcnn -> code for 2018 paper: RBox-CNN: rotated bounding box based CNN for ship detection in remote sensing image. Uses Tensorflow object detection API* Polar-Encodings -> code for 2021 paper* R-CenterNet -> detector for rotated-object based on CenterNet* piou -> Orientated Object Detection; IoU Loss, applied to DOTA dataset* DAFNe -> code for 2021 paper: DAFNe: A One-Stage Anchor-Free Approach for Oriented Object Detection* AProNet -> code for 2021 paper: AProNet: Detecting objects with precise orientation from aerial images. Applied to datasets DOTA and HRSC2016* UCAS-AOD-benchmark -> A benchmark of UCAS-AOD dataset* RotateObjectDetection -> based on Ultralytics/yolov5, with adjustments to enable rotate prediction boxes. Also see PolygonObjectDetection* AD-Toolbox -> Aerial Detection Toolbox based on MMDetection and MMRotate, with support for more datasets* GGHL -> code for 2022 paper: A General Gaussian Heatmap Label Assignment for Arbitrary-Oriented Object Detection* NPMMR-Det -> code for 2021 paper: A Novel Nonlocal-Aware Pyramid and Multiscale Multitask Refinement Detector for Object Detection in Remote Sensing Images* AOPG -> code for 2022 paper: Anchor-Free Oriented Proposal Generator for Object Detection* SE2-Det -> code for 2022 paper: Semantic-Edge-Supervised Single-Stage Detector for Oriented Object Detection in Remote Sensing Imagery* OrientedRepPoints -> code for 2021 paper: Oriented RepPoints for Aerial Object Detection* TS-Conv -> code for 2022 paper: Task-wise Sampling Convolutions for Arbitrary-Oriented Object Detection in Aerial Images

Object detection enhanced by super resolution

Super-Resolution and Object Detection -> Super-resolution is a relatively inexpensive enhancement that can improve object detection performance
EESRGAN -> Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network
Mid-Low Resolution Remote Sensing Ship Detection Using Super-Resolved Feature Representation
EESRGAN -> code for 2020 paper: Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network. Applied to COWC & OGST datasets
FBNet -> code for 2022 paper: Feature Balance for Fine-Grained Object Classification in Aerial Images
SuperYOLO -> code for 2022 paper: SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery

Salient object detection

Detecting the most noticeable or important object in a scene* ACCoNet -> code for 2022 paper: Adjacent Context Coordination Network for Salient Object Detection in Optical Remote Sensing Images* MCCNet -> Multi-Content Complementation Network for Salient Object Detection in Optical Remote Sensing Images* CorrNet -> Lightweight Salient Object Detection in Optical Remote Sensing Images via Feature Correlation. With paper* Reading list for deep learning based Salient Object Detection in Optical Remote Sensing Images* ORSSD-dataset -> salient object detection dataset* EORSSD-dataset -> Extended Optical Remote Sensing Saliency Detection (EORSSD) Dataset* DAFNet_TIP20 -> code for 2020 paper: Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images* EMFINet -> code for 2021 paper: Edge-Aware Multiscale Feature Integration Network for Salient Object Detection in Optical Remote Sensing Images* ERPNet -> code for 2022 paper: Edge-guided Recurrent Positioning Network for Salient Object Detection in Optical Remote Sensing Images* FSMINet -> code for 2022 paper: Fully Squeezed Multi-Scale Inference Network for Fast and Accurate Saliency Detection in Optical Remote Sensing Images* AGNet -> code for 2022 paper: AGNet: Attention Guided Network for Salient Object Detection in Optical Remote Sensing Images* MSCNet -> code for 2022 paper: A lightweight multi-scale context network for salient object detection in optical remote sensing images* GPnet -> code for 2022 paper: Global Perception Network for Salient Object Detection in Remote Sensing Images

Object detection - Buildings, rooftops & solar panels

satelliteimagetinhouse_detector -> Detection of tin houses from satellite/aerial images using the Tensorflow Object Detection API 🔰
Machine Learning For Rooftop Detection and Solar Panel Installment discusses tiling large images and generating annotations from OSM data. Features of the roofs were calculated using a combination of contour detection and classification. Follow up article using semantic segmentation
Building Extraction with YOLT2 and SpaceNet Data
AIcrowd dataset of building outlines -> 300x300 pixel RGB images with annotations in MS-COCO format
XBD-hurricanes -> Models for building (and building damage) detection in high-resolution (<1m) satellite and aerial imagery using a modified RetinaNet model
Detecting solar panels from satellite imagery using segmentation
ssd-spacenet -> Detect buildings in the Spacenet dataset using Single Shot MultiBox Detector (SSD)
3DBuildingInfoMap -> simultaneous extraction of building height and footprint from Sentinel imagery using ResNet
Solar Panel Detection -> using Faster R-CNN & Tensorflow object detection API. With repo
DeepSolaris -> a EuroStat project to detect solar panels in aerial images, further material here
MLObjectDetectionCAFO -> Detect Concentrated Animal Feeding Operations (CAFO) in Satellite Imagery
Multi-level-Building-Detection-Framework -> code for 2018 paper: Multilevel Building Detection Framework in Remote Sensing Images Based on Convolutional Neural Networks
Automatic Damage Annotation on Post-Hurricane Satellite Imagery -> detect damaged buildings using tensorflow object detection API. With repos here and here
mappingchallenge -> YOLOv5 applied to the AICrowd Mapping Challenge dataset

Object detection - Ships & boats

kaggle-ships-in-Google-Earth-yolov5 -> Applying YOLOv5 to Kaggle Ships in Google Earth dataset 🔰
How hard is it for an AI to detect ships on satellite images?
Object Detection in Satellite Imagery, a Low Overhead Approach
Detecting Ships in Satellite Imagery using the Planet dataset and Keras
Planet use non DL felzenszwalb algorithm to detect ships
Ship detection using k-means clustering & CNN classifier on patches
sentinel2-xcube-boat-detection -> detect and count boat traffic in Sentinel-2 imagery using temporal, spectral and spatial features
Arbitrary-Oriented Ship Detection through Center-Head Point Extraction -> arxiv paper. Keypoint estimation is performed to find the center of ships. Then, the size and head point of the ships are regressed. Repo ASD
ship_detection -> using an interesting combination of CNN classifier, Class Activation Mapping (CAM) & UNET segmentation. Accompanying three part blog post
Building a complete Ship detection algorithm using YOLOv3 and Planet satellite images -> covers finding and annotating data (using LabelMe), preprocessing large images into chips, and training Yolov3. Repo
Ship-detection-in-satellite-images -> experiments with UNET, YOLO, Mask R-CNN, SSD, Faster R-CNN, RETINA-NET
Ship-Detection-from-Satellite-Images-using-YOLOV4 -> uses Kaggle Airbus Ship Detection dataset
kaggle-airbus-ship-detection-challenge -> using oriented SSD
shipsnet-detector -> Detect container ships in Planet imagery using machine learning
Classifying Ships in Satellite Imagery with Neural Networks -> applied to the Kaggle Ships in Satellite Imagery dataset
Mask R-CNN for Ship Detection & Segmentation blog post with repo
contrastiveSSLship_detection -> Contrastive self supervised learning for ship detection in Sentinel 2 images
Boat detection with multi-region-growing method in satellite images
small-boat-detector -> Trained yolo v3 model weights and configuration file to detect small boats in satellite imagery
Satellite-Imagery-Datasets-Containing-Ships -> A list of optical and radar satellite datasets for ship detection, classification, semantic segmentation and instance segmentation tasks
Ship-Classification-in-Satellite-Images -> Convolutional neural network model for ship classification in satellite images
Ship-Detection -> CNN approach for ship detection in the ocean using a satellite image
vesselTracker -> Project based on reduced model of Yolov5 architecture using Pytorch. Custom dataset based on SAR imagery provided by Sentinel-1 through Earth Engine API
marine-debris-ml-model -> Marine Debris Detection using tensorflow object detection API
SDGH-Net -> code for 2021 paper: SDGH-Net: Ship Detection in Optical Remote Sensing Images Based on Gaussian Heatmap Regression
LR-TSDet -> code for 2021 paper: LR-TSDet: Towards Tiny Ship Detection in Low-Resolution Remote Sensing Images
FGSCR-42 -> A public Dataset for Fine-Grained Ship Classification in Remote sensing images
ShipDetection -> Ship Detection in HR Optical Remote Sensing Images via Rotated Bounding Box, based on Faster R-CNN and ORN, uses caffe
WakeNet -> A CNN-based optical image ship wake detector, code for 2021 paper: Rethinking Automatic Ship Wake Detection: State-of-the-Art CNN-based Wake Detection via Optical Images
Histogram of Oriented Gradients (HOG) Boat Heading Classification -> Medium article
Object Detection in Satellite Imagery, a Low Overhead Approach -> Medium article which demonstrates how to combine Canny edge detector pre-filters with HOG feature descriptors, random forest classifiers, and sliding windows to perform ship detection
simplifiedrboxcnn -> code for 2018 paper: RBox-CNN: rotated bounding box based CNN for ship detection in remote sensing image. Uses Tensorflow object detection API
Ship-Detection-based-on-YOLOv3-and-KV260 -> entry project of the Xilinx Adaptive Computing Challenge 2021. It uses YOLOv3 for ship target detection in optical remote sensing images, and deploys DPU on the KV260 platform to achieve hardware acceleration
LEVIR-Ship -> a dataset for tiny ship detection under medium-resolution remote sensing images
Push-and-Pull-Network -> code for 2022 paper: Contrastive Learning for Fine-grained Ship Classification in Remote Sensing Images
DRENet -> code for 2022 [paper])(https://ieeexplore.ieee.org/abstract/document/9791363): A Degraded Reconstruction Enhancement-Based Method for Tiny Ship Detection in Remote Sensing Images With a New Large-Scale Dataset
xView3-The-First-Place-Solution - A winning solution for xView 3 challenge (Vessel detection, classification and length estimation on Sentinetl-1 images). Contains trained models, inference pipeline and training code & configs to reproduce the results.
SARfish -> Ship detection in Sentinel 1 Synthetic Aperture Radar (SAR) imagery

Object detection - Cars, vehicles & trains

Detection of parkinglots and driveways with retinanet 🔰
pytorch-vedai -> object detection on the VEDAI dataset: Vehicle Detection in Aerial Imagery 🔰
Truck Detection with Sentinel-2 during COVID-19 crisis -> moving objects in Sentinel-2 data causes a specific reflectance relationship in the RGB, which looks like a rainbow, and serves as a marker for trucks. Improve accuracy by only analysing roads. Not using object detection but relevant. Also see S2TD
cowccarcounting -> car counting on the Cars Overhead With Context (COWC) dataset. Not sctictly object detection but a CNN to predict the car count in a tile
CarCounting -> using Yolov3 & COWC dataset
Traffic density estimation as a regression problem instead of object detection -> inspired by this paper
Applying Computer Vision to Railcar Detection -> useful insights into counting railcars (i.e. train carriages) using Mask-RCNN with rotated bounding boxes output
Leveraging Deep Learning for Vehicle Detection And Classification
Rotation-EfficientDet-D0 -> PyTorch implementation of Rotated EfficientDet, applied to a custom rotation vehicle dataset (car counting)
RSVC2021-Dataset -> A dataset for Vehicle Counting in Remote Sensing images, created from the DOTA & ITCVD Datasets
Car Localization and Counting with Overhead Imagery, an Interactive Exploration -> Medium article by Adam Van Etten
Vehicle-Counting-in-Very-Low-Resolution-Aerial-Images -> code for 2022 paper: Vehicle Counting in Very Low-Resolution Aerial Images via Cross-Resolution Spatial Consistency and Intraresolution Time Continuity

Object detection - Planes & aircraft

rareplanes-yolov5 -> using YOLOv5 and the RarePlanes dataset to detect and classify sub-characteristics of aircraft, with article 🔰
Faster RCNN to detect airplanes 🔰
yoltv4 includes examples on the RarePlanes dataset
aircraft-detection -> experiments to test the performance of a Gaussian process (GP) classifier with various kernels on the UC Merced land use land cover (LULC) dataset
The Rareplanes guide recommends annotating airplanes in a diamond style, which has several advantages (easily reproducible, convertible to a bounding box etc) and allows extracting the aircraft length and wingspan
Using Detectron2 to segment aircraft from satellite imagery -> pytorch and Rare Planes
aircraft-detection-from-satellite-images-yolov3 -> trained on kaggle cgi-planes-in-satellite-imagery-w-bboxes dataset
HRPlanesv2-Data-Set -> YOLOv4 and YOLOv5 weights trained on the HRPlanesv2 dataset
Deep-Learning-for-Aircraft-Recognition -> A CNN model trained to classify and identify various military aircraft through satellite imagery
FRCNN-for-Aircraft-Detection -> faster-rcnn & keras
ergo-planes-detector -> An ergo based project that relies on a convolutional neural network to detect airplanes from satellite imagery, uses the PlanesNet dataset
pytorch-remote-sensing -> Aircraft detection using the 'Airbus Aircraft Detection' dataset and Faster-RCNN with ResNet-50 backbone using pytorch
FasterRCNN_ObjectDetection -> faster RCNN model for aircraft detection and localisation in satellite images and creating a webpage with live server for public usage
HRPlanes -> weights of YOLOv4 and Faster R-CNN networks trained with HRPlanes dataset
aerial-detection -> uses Yolov5 & Icevision

Object detection - Infrastructure & utilities

wind-turbine-detector -> Wind Turbine Object Detection from Aerial Imagery Using TensorFlow Object Detection API
Water Tanks and Swimming Pools Detection -> uses Faster R-CNN
PCAN -> Part-Based Context Attention Network for Thermal Power Plant Detection in Remote Sensing Imagery, with dataset

Object detection - Animals

A variety of techniques can be used to count animals, including object detection and instance segmentation. For convenience they are all listed here:* cownterstrike -> counting cows, located with point-annotations, two models: CSRNet (a density-based method) & LCFCN (a detection-based method)* elephantdetection -> Using Keras-Retinanet to detect elephants from aerial images* CNN-Mosquito-Detection -> determining the locations of potentially dangerous breeding grounds, compared YOLOv4, YOLOR & YOLOv5* BorowiczetalSpacewhale -> locate whales using ResNet* walrus-detection-and-count -> uses Mask R-CNN instance segmentation* MarineMammalsDetection -> Weakly Supervised Detection of Marine Animals in High Resolution Aerial Images* Audubon_F21 -> code for 2022 paper: Deep object detection for waterbird monitoring using aerial imagery

Object tracking in videos

Object Tracking in Satellite Videos Based on a Multi-Frame Optical Flow Tracker arxiv paper
CFME -> Object Tracking in Satellite Videos by Improved Correlation Filters With Motion Estimations
TGraM -> code and dataset for 2022 paper: Multi-Object Tracking in Satellite Videos with Graph-Based Multi-Task Modeling
satellitevideomod_groundtruth -> groundtruth on satellite video for evaluating moving object detection algorithm
Moving-object-detection-DSFNet -> code for 2021 paper: DSFNet: Dynamic and Static Fusion Network for Moving Object Detection in Satellite Videos
HiFT -> code for 2021 paper: HiFT: Hierarchical Feature Transformer for Aerial Tracking
TCTrack -> code for 2022 paper: TCTrack: Temporal Contexts for Aerial Tracking

Counting trees

DeepForest is a python package for training and predicting individual tree crowns from airborne RGB imagery
Official repository for the "Identifying trees on satellite images" challenge from Omdena
Counting-Trees-using-Satellite-Images -> create an inventory of incoming and outgoing trees for an annual tree inspections, uses keras & semantic segmentation
2020 Nature paper - An unexpectedly large count of trees in the West African Sahara and Sahel -> tree detection framework based on U-Net & tensorflow 2 with code here
TreeDetection -> A color-based classifier to detect the trees in google image data along with tree visual localization and crown size calculations via OpenCV
PTDM -> code for 2022 paper: Pomelo Tree Detection Method Based on Attention Mechanism and Cross-Layer Feature Fusion
urban-tree-detection -> code for 2022 paper: Individual Tree Detection in Large-Scale Urban Environments using High-Resolution Multispectral Imagery. With dataset

Oil storage tank detection & oil spills

Oil is stored in tanks at many points between extraction and sale, and the volume of oil in storage is an important economic indicator.* A Beginner’s Guide To Calculating Oil Storage Tank Occupancy With Help Of Satellite Imagery* Oil Storage Tank’s Volume Occupancy On Satellite Imagery Using YoloV3 with repo* Oil-Tank-Volume-Estimation -> combines object detection and classical computer vision* MCAN-OilSpillDetection -> Oil Spill Detection with A Multiscale Conditional Adversarial Network under Small Data Training, with paper. A multiscale conditional adversarial network (MCAN) trained with four oil spill observation images accurately detects oil spills in new images.* Oil tank instance segmentation with Mask R-CNN with accompanying article using Keras & Airbus Oil Storage Detection Dataset on Kaggle* https://www.kaggle.com/towardsentropy/oil-storage-tanks -> large kaggle dataset, note however that approx 85% of images contain no tanks* https://www.kaggle.com/airbusgeo/airbus-oil-storage-detection-dataset -> smaller kaggle dataset* ognet -> a Global Oil and Gas Infrastructure Database using Deep Learning on Remotely Sensed Imagery* RSOD-Dataset -> dataset for object detection in PASCAL VOC format. Aircraft, playgrounds, overpasses & oiltanks. Used in the 2022 paper: Improved YOLOv5 network method for remote sensing image-based ground objects recognition* oilstorage-detector -> using yolov5 and the Airbus Oil Storage Detection dataset* oilwell_detector -> detect oil wells in the Bakken oil field based on satellite imagery* OGST -> Oil and Gas Tank Dataset* AContrarioTankDetection -> code for 2020 paper: Oil Tank Detection in Satellite Images via a Contrario Clustering* SubpixelCircleDetection -> code for 2020 paper: CIRCULAR-SHAPED OBJECT DETECTION IN LOW RESOLUTION SATELLITE IMAGES* Oil Storage Detection on Airbus Imagery with YOLOX -> uses the Kaggle Airbus Oil Storage Detection dataset

Cloud detection & removal

There are many algorithms that use band math to detect clouds, but the deep learning approach is to use semantic segmentation* CloudSEN12 -> Sentinel 2 cloud dataset with a varierty of models here* See section Kaggle - Understanding Clouds from Satellite Images* From this article on sentinelhub there are three popular classical algorithms that detects thresholds in multiple bands in order to identify clouds. In the same article they propose using semantic segmentation combined with a CNN for a cloud classifier (excellent review paper here), but state that this requires too much compute resources.* This article compares a number of ML algorithms, random forests, stochastic gradient descent, support vector machines, Bayesian method.* Segmentation of Clouds in Satellite Images Using Deep Learning -> semantic segmentation using a Unet on the Kaggle 38-Cloud dataset* Cloud Detection in Satellite Imagery compares FPN+ResNet18 and CheapLab architectures on Sentinel-2 L1C and L2A imagery* Benchmarking Deep Learning models for Cloud Detection in Landsat-8 and Sentinel-2 images* Landsat-8 to Proba-V Transfer Learning and Domain Adaptation for Cloud detection* Multitemporal Cloud Masking in Google Earth Engine* s2cloudmask -> Sentinel-2 Cloud and Shadow Detection using Machine Learning* sentinel2-cloud-detector -> Sentinel Hub Cloud Detector for Sentinel-2 images in Python* dsen2-cr -> cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion, contains the model code, written in Python/Keras, as well as links to pre-trained checkpoints and the SEN12MS-CR dataset* pyatsa -> Python package implementing the Automated Time-Series Analysis method for masking clouds in satellite imagery developed by Zhu and Helmer 2018* decloud -> Decloud enables the training of various deep nets to remove clouds in optical image, using e.g. Sentinel 1 & 2* cloudless -> Deep learning pipeline for orbital satellite data for detecting clouds* Deep-Gapfill -> Official implementation of Optical image gap filling using deep convolutional autoencoder from optical and radar images* satellite-cloud-removal-dip -> Satellite cloud removal with Deep Image Prior, with paper* cloudFCN -> Python 3 package for Fully Convolutional Network development, specifically for cloud masking* Fmask -> Fmask (Function of mask) is used for automated clouds, cloud shadows, snow, and water masking for Landsats 4-9 and Sentinel 2 images, in Matlab. Also see PyFmask* HOW TO USE DEEP LEARNING, PYTORCH LIGHTNING, AND THE PLANETARY COMPUTER TO PREDICT CLOUD COVER IN SATELLITE IMAGERY* cloud-cover-winners -> Code from the winning submissions for the On Cloud N: Cloud Cover Detection Challenge* On-Cloud-N: Cloud Cover Detection Challenge - 19th Place Solution* ukis-csmask -> package to masks clouds in Sentinel-2, Landsat-8, Landsat-7 and Landsat-5 images* OpenSICDR -> long list of satellite image cloud detection resources* RS-Net -> code for the paper: A cloud detection algorithm for satellite imagery based on deep learning* Clouds-Segmentation-Project -> treats as a 3 class problem; Open clouds, Closed clouds and no clouds, uses pytorch on a dataset that consists of IR & Visual Grayscale images* STGAN -> PyTorch Implementation of STGAN for Cloud Removal in Satellite Images, with paper* mcgan-cvprw2017-pytorch -> code for 2017 paper: Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets* Cloud-Net: A semantic segmentation CNN for cloud detection -> an end-to-end cloud detection algorithm for Landsat 8 imagery, trained on 38-Cloud Training Set* fcd -> code for 2021 paper: Fixed-Point GAN for Cloud Detection. A weakly-supervised approach, training with only image-level labels* CloudX-Net -> an efficient and robust architecture used for detection of clouds from satellite images* A simple cloud-detection walk-through using Convolutional Neural Network (CNN and U-Net) and fast.ai library* 38Cloud-Medium -> Walk-through using u-net to detect clouds in satellite images with fast.ai* clouddetectionusingsatellitedata -> performed on Sentinel 2 data* Luojia1-Cloud-Detection -> Luojia-1 Satellite Visible Band Nighttime Imagery Cloud Detection* SEN12MS-CR-TS -> code for 2022 paper: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud Removal* ES-CCGAN -> This is a dehazed method for remote sensing image, which based on CycleGAN* CloudClassificationDL -> Classifying cloud organization patterns from satellite images using Deep Learning techniques (Mask R-CNN)* CNN-based-Cloud-Detection-Methods -> Understanding the Role of Receptive Field of Convolutional Neural Network for Cloud Detection in Landsat 8 OLI Imagery* cloud-removal-deploy -> flask app for cloud removal* CloudMattingGAN -> code for 2019 paper: Generative Adversarial Training for Weakly Supervised Cloud Matting* atrain-cloudseg -> Official repository for the A-Train Cloud Segmentation Dataset* CDnet -> code for 2019 paper: CNN-Based Cloud Detection for Remote Sensing Imager* GLNET -> code for 2021 paper: Convolutional Neural Networks Based Remote Sensing Scene Classification under Clear and Cloudy Environments* CDnetV2 -> code for 2021 paper: CNN-Based Cloud Detection for Remote Sensing Imagery With Cloud-Snow Coexistence* grouped-features-alignment -> code for 2021 paper: Unsupervised Domain Adaptation for Cloud Detection Based on Grouped Features Alignment and Entropy Minimization* Detecting Cloud Cover Via Sentinel-2 Satellite Data -> blog post on Benjamin Warners Top-10 Percent Solution to DrivenData’s On CloudN Competition using fast.ai & customized version of XResNeXt50. Repo* AISD -> code (Matlab) and dataset for 2020 paper: Deeply supervised convolutional neural network for shadow detection based on a novel aerial shadow imagery dataset* CloudGAN -> Detecting and Removing Clouds from RGB-images using Image Inpainting* Using GANs to Augment Data for Cloud Image Segmentation Task -> code for 2021 paper* Cloud-Segmentation-from-Satellite-Imagery -> applied to Sentinel-2 dataset* HRC_WHU -> High-Resolution Cloud Detection Dataset comprising 150 RGB images and a resolution varying from 0.5 to 15 m in different global regions* MEcGANs -> Cloud Removal from Satellite Imagery using Multispectral Edge-filtered Conditional Generative Adversarial Networks* CloudXNet -> code for 2020 paper: CloudX-net: A robust encoder-decoder architecture for cloud detection from satellite remote sensing images* refined-unet-lite -> code for 2022 paper: Refined UNet Lite: End-to-End Lightweight Network for Edge-precise Cloud Detection* cloud-buster -> Sentinel-2 L1C and L2A Imagery with Fewer Clouds* SatelliteCloudGenerator -> A PyTorch-based tool to generate clouds for satellite images* SEnSeI -> A python 3 package for developing sensor independent deep learning models for cloud masking in satellite imagery

Change detection

Generally speaking, change detection methods are applied to a pair of images to generate a mask of change, e.g. of buildings damaged in a disaster. Note, clouds & shadows change often too..!* awesome-remote-sensing-change-detection lists many datasets and publications* Change-Detection-Review -> A review of change detection methods, including code and open data sets for deep learning* Unstructured-change-detection-using-CNN* Siamese neural network to detect changes in aerial images -> uses Keras and VGG16 architecture* Change Detection in 3D: Generating Digital Elevation Models from Dove Imagery* QGIS plugin for applying change detection algorithms on high resolution satellite imagery* LamboiseNet -> Master thesis about change detection in satellite imagery using Deep Learning* Fully Convolutional Siamese Networks for Change Detection -> with paper* Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks -> with paper, used the Onera Satellite Change Detection (OSCD) dataset* STANet -> official implementation of the spatial-temporal attention neural network (STANet) for remote sensing image change detection* BITCD -> Official Pytorch Implementation of Remote Sensing Image Change Detection with Transformers* IAugCDNet -> Official Pytorch Implementation of Adversarial Instance Augmentation for Building Change Detection in Remote Sensing Images* dpm-rnn-public -> Code implementing a damage mapping method combining satellite data with deep learning* SenseEarth2020-ChangeDetection -> 1st place solution to the Satellite Image Change Detection Challenge hosted by SenseTime; predictions of five HRNet-based segmentation models are ensembled, serving as pseudo labels of unchanged areas* KPCAMNet -> Python implementation of the paper Unsupervised Change Detection in Multi-temporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network* CDLab -> benchmarking deep learning-based change detection methods.* Siam-NestedUNet -> The pytorch implementation for "SNUNet-CD: A Densely Connected Siamese Network for Change Detection of VHR Images"* SUNet-changedetection -> Implementation of paper SUNet: Change Detection for Heterogeneous Remote Sensing Images from Satellite and UAV Using a Dual-Channel Fully Convolution Network* Self-supervised Change Detection in Multi-view Remote Sensing Images* MFPNet -> Remote Sensing Change Detection Based on Multidirectional Adaptive Feature Fusion and Perceptual Similarity* GitHub for the DIUx xView Detection Challenge -> The xView2 Challenge focuses on automating the process of assessing building damage after a natural disaster* DASNet -> Dual attentive fully convolutional siamese networks for change detection of high-resolution satellite images* Self-Attention for Raw Optical Satellite Time Series Classification* planet-movement -> Find and process Planet image pairs to highlight object movement* UNet-based-Unsupervised-Change-Detection -> A convolutional neural network (CNN) and semantic segmentation is implemented to detect the changes between the images, as well as classify the changes into the correct semantic class, with arxiv paper* temporal-cluster-matching -> detecting change in structure footprints from time series of remotely sensed imagery* autoRIFT -> fast and intelligent algorithm for finding the pixel displacement between two images* DSAMNet -> Code for “A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection”. The main types of changes in the dataset include: (a) newly built urban buildings; (b) suburban dilation; (c) groundwork before construction; (d) change of vegetation; (e) road expansion; (f) sea construction.* SRCDNet -> The pytorch implementation for "Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different Resolutions ". SRCDNet is designed to learn and predict change maps from bi-temporal images with different resolutions* Land-Cover-Analysis -> Land Cover Change Detection using Satellite Image Segmentation* A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sening images* Satellite-Image-Alignment-Differencing-and-Segmentation -> thesis on change detection* Change Detection in Multi-temporal Satellite Images -> uses Principal Component Analysis (PCA) and K-means clustering* Unsupervised Change Detection Algorithm using PCA and K-Means Clustering -> in Matlab but has paper* ChangeFormer -> A Transformer-Based Siamese Network for Change Detection. Uses transformer architecture to address the limitations of CNN in handling multi-scale long-range details. Demonstrates that ChangeFormer captures much finer details compared to the other SOTA methods, achieving better performance on benchmark datasets* HeterogeneousCD -> Heterogeneous Change Detection in Remote Sensing Images. Accompanies Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images* ChangeDetectionProject -> Trying out Active Learning in with deep CNNs for Change detection on remote sensing data* DSFANet -> Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images* siamese-change-detection -> Targeted synthesis of multi-temporal remote sensing images for change detection using siamese neural networks* Bi-SRNet -> code for 2022 paper: Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images* SiROC -> Implementation of the paper Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images. Applied to Sentinel-2 and high-resolution Planetscope imagery on four datasets* DSMSCN -> Tensorflow implementation for Change Detection in Multi-temporal VHR Images Based on Deep Siamese Multi-scale Convolutional Neural Networks* RaVAEn -> a lightweight, unsupervised approach for change detection in satellite data based on Variational Auto-Encoders (VAEs) with the specific purpose of on-board deployment. It flags changed areas to prioritise for downlink, shortening the response time* SemiCD -> Code for paper: Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images. Achieves the performance of supervised CD even with access to as little as 10% of the annotated training data* FCCDNpytorch -> code for paper: FCCDN: Feature Constraint Network for VHR Image Change Detection. Uses the LEVIR-CD building change detection dataset* INLPGPython -> code for paper: Structure Consistency based Graph for Unsupervised Change Detection with Homogeneous and Heterogeneous Remote Sensing Images* NSPGPython -> code for paper: Nonlocal patch similarity based heterogeneous remote sensing change detection* LGPNet-BCD -> code for 2021 paper: Building Change Detection for VHR Remote Sensing Images via Local-Global Pyramid Network and Cross-Task Transfer Learning Strategy* DSUNet -> code for 2021 paper: Sentinel-1 and Sentinel-2 Data Fusion for Urban Change Detection using a Dual Stream U-Net, uses Onera Satellite Change Detection dataset* SiameseSSL -> code for 2022 paper: Urban change detection with a Dual-Task Siamese network and semi-supervised learning. Uses SpaceNet 7 dataset* CD-SOTA-methods -> Remote sensing change detection: State-of-the-art methods and available datasets* multimodalCDISPRS21 -> code for 2021 paper: Fusing Multi-modal Data for Supervised Change Detection* Unsupervised-CD-in-SITS-using-DL-and-Graphs -> code for article: Unsupervised Change Detection Analysis in Satellite Image Time Series using Deep Learning Combined with Graph-Based Approaches* LSNet -> code for 2022 paper: Extremely Light-Weight Siamese Network For Change Detection in Remote Sensing Image* Change-Detection-in-Remote-Sensing-Images -> using PCA & K-means* End-to-end-CD-for-VHR-satellite-image -> code for 2019 paper: End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++* Semantic-Change-Detection -> code for 2021 paper: SCDNET: A novel convolutional network for semantic change detection in high resolution optical remote sensing imagery* ERCNN-DRSurbanchangemonitoring -> code for 2021 paper: Neural Network-Based Urban Change Monitoring with Deep-Temporal Multispectral and SAR Remote Sensing Data* EGRCNN -> code for 2021 paper: Edge-guided Recurrent Convolutional Neural Network for Multi-temporal Remote Sensing Image Building Change Detection* Unsupervised-Remote-Sensing-Change-Detection -> code for 2021 paper: An Unsupervised Remote Sensing Change Detection Method Based on Multiscale Graph Convolutional Network and Metric Learning* CropLand-CD -> code for 2022 paper: A CNN-transformer Network with Multi-scale Context Aggregation for Fine-grained Cropland Change Detection* contrastive-surface-image-pretraining -> code for 2022 paper: Supervising Remote Sensing Change Detection Models with 3D Surface Semantics* dcvaVHROptical -> Deep Change Vector Analysis (DCVA) change detection. Code for 2019 paper: Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images* hyperdimensionalCD -> code for 2021 paper: Change Detection in Hyperdimensional Images Using Untrained Models* DSFANet -> code for 2018 paper: Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images* FCD-GAN-pytorch -> Fully Convolutional Change Detection Framework with Generative Adversarial Network (FCD-GAN) is a framework for change detection in multi-temporal remote sensing images* DARNet-CD -> code for 2022 paper: A Densely Attentive Refinement Network for Change Detection Based on Very-High-Resolution Bitemporal Remote Sensing Images* xView2Vulcan -> Damage assessment using pre and post orthoimagery. Modified + productionized model based off the first-place model from the xView2 challenge.* ESCNet -> code for 2021 paper: An End-to-End Superpixel-Enhanced Change Detection Network for Very-High-Resolution Remote Sensing Images* ForestCoverChange -> Detecting and Predicting Forest Cover Change in Pakistani Areas Using Remote Sensing Imagery* deforestation-detection -> code for 2020 paper: DEEP LEARNING FOR HIGH-FREQUENCY CHANGE DETECTION IN UKRAINIAN FOREST ECOSYSTEM WITH SENTINEL-2* forestchangedetection -> forest change segmentation with time-dependent models, including Siamese, UNet-LSTM, UNet-diff, UNet3D models. Code for 2021 paper: Deep Learning for Regular Change Detection in Ukrainian Forest Ecosystem With Sentinel-2* SentinelClearcutDetection -> Scripts for deforestation detection on the Sentinel-2 Level-A images* clearcutdetection -> research & web-service for clearcut detection* CDRL -> code for 2022 paper: Unsupervised Change Detection Based on Image Reconstruction Loss* ddpm-cd -> code for 2022 paper: Remote Sensing Change Detection (Segmentation) using Denoising Diffusion Probabilistic Models* Remote-sensing-time-series-change-detection -> code for 2022 paper: Graph-based block-level urban change detection using Sentinel-2 time series* austin-ml-change-detection-demo -> A change detection demo for the Austin area using a pre-trained PyTorch model scaled with Dask on Planet imagery* dfc2021-msd-baseline -> A baseline for the "Multitemporal Semantic Change Detection" track of the 2021 IEEE GRSS Data Fusion Competition* CorrFusionNet -> code for 2020 paper: Multi-Temporal Scene Classification and Scene Change Detection with Correlation based Fusion* ChangeDetectionPCAKmeans -> MATLAB implementation for Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and k-Means Clustering.* IRCNN -> code for 2022 paper: IRCNN: An Irregular-Time-Distanced Recurrent Convolutional Neural Network for Change Detection in Satellite Time Series* UTRNet -> An Unsupervised Time-Distance-Guided Convolutional Recurrent Network for Change Detection in Irregularly Collected Images* open-cd -> an open source change detection toolbox based on a series of open source general vision task tools* Tinymodel4CD -> code for 2022 paper: TINYCD: A (Not So) Deep Learning Model For Change Detection. Uses LEVIR-CD & WHU-CD datasets* FHD -> code for 2022 paper: Feature Hierarchical Differentiation for Remote Sensing Image Change Detection* Change detection with Raster Vision -> blog post with Colab notebook* building-expansion -> code for 2021 paper: Enhancing Environmental Enforcement with Near Real-Time Monitoring: Likelihood-Based Detection of Structural Expansion of Intensive Livestock Farms* SaDLCD -> code for 2022 paper: Semantic-aware Dense Representation Learning for Remote Sensing Image Change Detection* EGCTNet_pytorch -> code for 2022 paper: Building Change Detection Based on an Edge-Guided Convolutional Neural Network Combined with a Transformer* S2-cGAN -> code for 2020 paper: S2-cGAN: Self-Supervised Adversarial Representation Learning for Binary Change Detection in Multispectral Images

Time series

More general than change detection, time series observations can be used for applications including improving the accuracy of crop classification, or predicting future patterns & events. Crop yield is very typically application and has its own section below* CropDetectionDL -> using GRU-net, First place solution for Crop Detection from Satellite Imagery competition organized by CV4A workshop at ICLR 2020* LANDSAT Time Series Analysis for Multi-temporal Land Cover Classification using Random Forest* temporalCNN -> Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series* pytorch-psetae -> PyTorch implementation of the model presented in Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention* satflow -> optical flow models for predicting future satellite images from current and past ones* esa-superresolution-forecasting -> Forecasting air pollution using ESA Sentinel-5p data, and an encoder-decoder convolutional LSTM neural network architecture, implemented in Pytorch* Radiant-Earth-Spot-the-Crop-Challenge -> The main objective of this challenge was to use time-series of Sentinel-2 multi-spectral data to classify crops in the Western Cape of South Africa. The challenge was to build a machine learning model to predict crop type classes for the test dataset* lightweight-temporal-attention-pytorch -> A PyTorch implementation of the Light Temporal Attention Encoder (L-TAE) for satellite image time series. classification* Crop-Classification -> crop classification using multi temporal satellite images* dtwSat -> Time-Weighted Dynamic Time Warping for satellite image time series analysis* DeepCropMapping -> A multi-temporal deep learning approach with improved spatial generalizability for dynamic corn and soybean mapping, uses LSTM* CropMappingInterpretation -> An interpretation pipeline towards understanding multi-temporal deep learning approaches for crop mapping* MTLCC -> code for paper: Multitemporal Land Cover Classification Network. A recurrent neural network approach to encode multi-temporal data for land cover classification* timematch -> code for 2022 paper: A method to perform unsupervised cross-region adaptation of crop classifiers trained with satellite image time series. We also introduce an open-access dataset for cross-region adaptation with SITS from four different regions in Europe* PWWB -> Code for the 2021 paper: Real-Time Spatiotemporal Air Pollution Prediction with Deep Convolutional LSTM through Satellite Image Analysis* Classification of Crop Fields through Satellite Image Time Series -> using a pytorch-psetae & Sentinel-2 data* spaceweather -> predicting geomagnetic storms from satellite measurements of the solar wind and solar corona, uses LSTMs* ForestwildfirespreadingconvLSTM -> Modeling of the spreading of forest wildfire using a neural network with ConvLSTM cells. Prediction 3-days forward* ConvTimeLSTM -> Extension of ConvLSTM and Time-LSTM for irregularly spaced images, appropriate for Remote Sensing* dl-time-series -> Deep Learning algorithms applied to characterization of Remote Sensing time-series* tpe -> code for 2022 paper: Generalized Classification of Satellite Image Time Series With Thermal Positional Encoding* wildfireforecasting -> code for 2021 paper: Deep Learning Methods for Daily Wildfire Danger Forecasting. Uses ConvLSTM* satelliteimageforecasting -> predict future satellite images from past ones using features such as precipitation and elevation maps. Entry for the EarthNet2021 challenge

Crop yield

Crop yield Prediction with Deep Learning -> code for the paper Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data
Deep-Transfer-Learning-Crop-Yield-Prediction
Crop-Yield-Prediction-using-ML -> A simple Web application developed in order to provide the farmers/users an approximation on how much amount of crop yield will be produced depending upon the given input
Building a Crop Yield Prediction App in Senegal Using Satellite Imagery and Jupyter Voila
Crop Yield Prediction Using Deep Neural Networks and LSTM
Deep transfer learning techniques for crop yield prediction, published in COMPASS 2018
Understanding crop yield predictions from CNNs
Advanced Deep Learning Techniques for Predicting Maize Crop Yield using Sentinel-2 Satellite Imagery
pycrop-yield-prediction -> A PyTorch Implementation of Jiaxuan You's Deep Gaussian Process for Crop Yield Prediction
PredictYield -> using data scraped from Google Earth Engine, this predicts the yield of Corn, Soybean, and Wheat in the USA with Keras
Crop-Yield-Prediction-and-Estimation-using-Time-series-remote-sensing-data -> student research
Yield-Prediction-Using-Sentinel-Data
SPACY -> Satellite Prediction of Aggregate Corn Yield
cropyieldArticle -> code for 2022 paper: Scalable Crop Yield Prediction with Sentinel-2 Time Series and Temporal Convolutional Network

Wealth and economic activity

The goal is to predict economic activity from satellite imagery rather than conducting labour intensive ground surveys* Using publicly available satellite imagery and deep learning to understand economic well-being in Africa, Nature Comms 22 May 2020 -> Used CNN on Ladsat imagery (night & day) to predict asset wealth of African villages* Combining Satellite Imagery and machine learning to predict poverty -> review article* Measuring Human and Economic Activity from Satellite Imagery to Support City-Scale Decision-Making during COVID-19 Pandemic -> arxiv article* Predicting Food Security Outcomes Using CNNs for Satellite Tasking -> arxiv article* Measuring the Impacts of Poverty Alleviation Programs with Satellite Imagery and Deep Learning -> code and paper* Building a Spatial Model to Classify Global Urbanity Levels -> estimage global urbanity levels from population data, nightime lights and road networks* deeppop -> Deep Learning Approach for Population Estimation from Satellite Imagery, also on Github* Estimating telecoms demand in areas of poor data availability -> with papers on arxiv and Science Direct* satimage -> Code and models for the manuscript "Predicting Poverty and Developmental Statistics from Satellite Images using Multi-task Deep Learning". Predict the main material of a roof, source of lighting and source of drinking water for properties, from satellite imagery* africapoverty -> Using publicly available satellite imagery and deep learning to understand economic well-being in Africa* Predicting-Poverty -> Combining satellite imagery and machine learning to predict poverty, in PyTorch* income-prediction -> Predicting average yearly income based on satellite imagery using CNNs, uses pytorch* urbanscore -> Pytorch Implementation of paper: Learning to score economic development from satellite imagery* READ -> Pytorch Implementation of paper: Lightweight and robust representation of economic scales from satellite imagery* Slum-classification -> Binary classification on a very high-resolution satellite image in case of mapping informal settlements using unet* PredictingPoverty -> uses daytime & luminosity of nighttime satellite images* Cancer-Prevalence-Satellite-Images -> Predict Health Outcomes from Features of Satellite Images* Mapping Poverty in Bangladesh with Satellite Images and Deep Learning -> combines health data with OpenStreetMaps Data & night and daytime satellite imagery* Population Estimation from Satellite Imagery* DeepLearningSatelliteImd -> code for "Project Bhoomi" - Using Deep Learning on Satellite Imagery to predict population and economic indicators* satelliteledliverpool -> code for 2017 paper: Remote Sensing-Based Measurement of Living Environment Deprivation - Improving Classical Approaches with Machine Learning* uganda-poverty-project -> use through Object Detection on high-resolution satellite imagery to identify indicators of poverty and economic inequality within Uganda* PredictingEnergyConsumptionWithConvolutionalNeuralNetworks

Disaster response

Also checkout the sections on change detection and water/fire/building segmentation* DisaVu -> combines building & damage detection and provides an app for viewing predictions* Soteria -> uses machine learning with satellite imagery to map natural disaster impacts for faster emergency response* DisasterHack -> Wildfire Mitigation: Computer Vision Identification of Hazard Fuels Using Landsat* forestcasting -> Forest fire prediction powered by analytics* Machine Learning-based Damage Assessment for Disaster Relief on Google AI blog -> uses object detection to locate buildings, then a classifier to determine if a building is damaged. Challenge of generalising due to small dataset* hurricanedamage -> Post-hurricane structure damage assessment based on aerial imagery with CNN* rescue -> code of the paper: Attention to fires: multi-channel deep-learning models forwildfire severity prediction* Disaster-Classification -> A disaster classification model to predict the type of disaster given an input image, trained on this dataset* Coarse-to-fine weakly supervised learning method for green plastic cover segmentation -> with paper* Detection of destruction in satellite imagery* BDD-Net -> code for 2020 paper: A General Protocol for Mapping Buildings Damaged by a Wide Range of Disasters Based on Satellite Imagery* AutomaticDisasterDetection -> detect the affected area by natural disasters by using the way of semantic segmentation and change detection method* Flooding Damage Detection from Post-Hurricane Satellite Imagery Based on Convolutional Neural Networks* IBM-Disaster-Response-Hack -> identifying optimal terrestrial routes through calamity-stricken areas. Satellite image data informs road condition assessment and obstruction detection* Automatic Damage Annotation on Post-Hurricane Satellite Imagery -> detect damaged buildings using tensorflow object detection API. With repos here and here* Hurricane-Damage-Detection -> Waterloo's Hack the North 2020++ submission. A convolutional neural network model used to detect hurricane damage in RGB satellite images* wildfireforecasting -> code for 2021 paper: Deep Learning Methods for Daily Wildfire Danger Forecasting. Uses ConvLSTM* Satellite Image Analysis with fast.ai for Disaster Recovery* shackleton -> leverages remote sensing imagery and machine learning techniques to provide insights into various transportation and evacuation scenarios in an interactive dashboard that conducts real-time computation

Weather phenomena

EddyData -> code for paper: A Deep Framework for Eddy Detection and Tracking from Satellite Sea Surface Height Data
python-windspeed -> Predicting windspeed of hurricanes from satellite images, uses CNN regression in keras
hurricane-wind-speed-cnn -> Predicting windspeed of hurricanes from satellite images, uses CNN regression in keras

Super-resolution

Super-resolution attempts to enhance the resolution of an imaging system, and can be applied as a pre-processing step to improve the detection of small objects or boundaries. Its use is controversial since it can introduce artefacts at the same rate as real features. These techniques are generally grouped into single image super resolution (SISR) or a multi image super resolution (MISR)* The value of super resolution — real world use case -> Medium article on parcel boundary detection with super-resolved satellite imagery* Super-Resolution on Satellite Imagery using Deep Learning -> Nov 2016 blog post by CosmiQ Works with a nice introduction to the topic. Proposes and demonstrates a new architecture with perturbation layers with practical guidance on the methodology and code. Three part series* Introduction to spatial resolution* Awesome-Super-Resolution -> another 'awesome' repo, getting a little out of date now* Super-Resolution (python) Utilities for managing large satellite images* pytorch-enhance -> Library of Image Super-Resolution Models, Datasets, and Metrics for Benchmarking or Pretrained Use. Also checkout this implementation in Jax* Super Resolution in OpenCV* AI-based Super resolution and change detection to enforce Sentinel-2 systematic usage -> Worldview-2 images (2m) were used to create a reference dataset and increase the spatial resolution of the Copernicus sensor from 10m to 5m* SRCDNet -> The pytorch implementation for "Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different Resolutions ". SRCDNet is designed to learn and predict change maps from bi-temporal images with different resolutions* Model-Guided Deep Hyperspectral Image Super-resolution -> code accompanying the paper Model-Guided Deep Hyperspectral Image Super-Resolution* Super-resolving beyond satellite hardware -> paper assessing SR performance in reconstructing realistically degraded satellite images* satellite-pixel-synthesis-pytorch -> PyTorch implementation of NeurIPS 2021 paper: Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis* SRE-HAN -> Squeeze-and-Residual-Excitation Holistic Attention Network improves super-resolution (SR) on remote-sensing imagery compared to other state-of-the-art attention-based SR models* satsr -> A project to perform super-resolution on multispectral images from any satellite, including Sentinel 2, Landsat 8, VIIRS &MODIS* OLI2MSI -> dataset for remote sensing imagery super-resolution composed of Landsat8-OLI and Sentinel2-MSI images* MMSR -> Learning Mutual Modulation for Self-Supervised Cross-Modal Super-Resolution* HSRnet -> code for the 2021 paper: Hyperspectral Image Super-resolution via Deep Spatio-spectral Attention Convolutional Neural Networks* RRSGAN -> code for 2021 paper: RRSGAN: Reference-Based Super-Resolution for Remote Sensing Image* HDR-DSP-SR -> code for 2021 paper: Self-supervised multi-image super-resolution for push-frame satellite images

Single image super-resolution (SISR)

Super Resolution for Satellite Imagery - srcnn repo
TensorFlow implementation of "Accurate Image Super-Resolution Using Very Deep Convolutional Networks" adapted for working with geospatial data
Random Forest Super-Resolution (RFSR repo) including sample data
Enhancing Sentinel 2 images by combining Deep Image Prior and Decrappify. Repo for deep-image-prior and article on decrappify
Image Super-Resolution using an Efficient Sub-Pixel CNN -> the keras docs have a great tutorial on this light weight but well performing model
super-resolution-using-gan -> Super-Resolution of Sentinel-2 Using Generative Adversarial Networks
Super-resolution of Multispectral Satellite Images Using Convolutional Neural Networks with paper
Multi-temporal Super-Resolution on Sentinel-2 Imagery using HighRes-Net, repo
SSPSR-Pytorch -> A spatial-spectral prior deep network for single hyperspectral image super-resolution
Sentinel-2 Super-Resolution: High Resolution For All (Bands)
CinCGAN -> Unofficial Implementation of Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks
Satellite-image-SRGAN using PyTorch
EEGAN -> Edge Enhanced GAN For Remote Sensing Image Super-Resolution, TensorFlow 1.1
PECNN -> A Progressively Enhanced Network for Video Satellite Imagery Super-Resolution, minimal documentation
hs-sr-tvtv -> Enhanced Hyperspectral Image Super-Resolution via RGB Fusion and TV-TV Minimization
sr4rs -> Super resolution for remote sensing, with pre-trained model for Sentinel-2, SRGAN-inspired
Restoring old aerial images with Deep Learning -> Medium article on Super Resolution with Perceptual Loss function and real images as input
RFSR_TGRS -> code for the paper Hyperspectral Image Super-Resolution via Recurrent Feedback Embedding and Spatial-Spectral Consistency Regularization
SEN2VENµS -> a dataset for the training of Sentinel-2 super-resolution algorithms. With paper
TransENet -> code for 2021 paper: Transformer-based Multi-Stage Enhancement for Remote Sensing Image Super-Resolution
SG-FBGAN -> code for 2020 paper: Remote Sensing Image Super-Resolution via Saliency-Guided Feedback GANs
finetune_ESRGAN -> finetune the ESRGAN super resolution generator for remote sensing images and video
MIP -> code for 2021 paper: Unsupervised Remote Sensing Super-Resolution via Migration Image Prior
Optical-RemoteSensing-Image-Resolution -> code for 2018 paper: Deep Memory Connected Neural Network for Optical Remote Sensing Image Restoration. Two applications: Gaussian image denoising and single image super-resolution
HSENet -> code for 2021 paper: Hybrid-Scale Self-Similarity Exploitation for Remote Sensing Image Super-Resolution
SR_RemoteSensing -> Super-Resolution deep learning models for remote sensing data based on BasicSR
RSI-Net -> code for 2022 paper: A Deep Multi-task Convolutional Neural Network for Remote Sensing Image Super-resolution and Colorization
EDSR-Super-Resolution -> EDSR model using PyTorch applied to satellite imagery
CycleCNN -> code for 2021 paper: Nonpairwise-Trained Cycle Convolutional Neural Network for Single Remote Sensing Image Super-Resolution
SISR with with Real-World Degradation Modeling -> code for 2022 paper: Single-Image Super Resolution of Remote Sensing Images with Real-World Degradation Modeling
pixel-smasher -> code for 2020 paper: Super-Resolution Surface Water Mapping on the Canadian Shield Using Planet CubeSat Images and a Generative Adversarial Network

Multi image super-resolution (MISR)

Note that nearly all the MISR publications resulted from the PROBA-V Super Resolution competition* deepsum -> Deep neural network for Super-resolution of Unregistered Multitemporal images (ESA PROBA-V challenge)* 3DWDSRNet -> code to reproduce Satellite Image Multi-Frame Super Resolution (MISR) Using 3D Wide-Activation Neural Networks* RAMS -> Official TensorFlow code for paper Multi-Image Super Resolution of Remotely Sensed Images Using Residual Attention Deep Neural Networks* TR-MISR -> Transformer-based MISR framework for the the PROBA-V super-resolution challenge. With paper* HighRes-net -> Pytorch implementation of HighRes-net, a neural network for multi-frame super-resolution, trained and tested on the European Space Agency’s Kelvin competition* ProbaVref -> Repurposing the Proba-V challenge for reference-aware super resolution* The missing ingredient in deep multi-temporal satellite image super-resolution -> Permutation invariance harnesses the power of ensembles in a single model, with repo piunet* MSTT-STVSR -> Space-time Super-resolution for Satellite Video: A Joint Framework Based on Multi-Scale Spatial-Temporal Transformer, JAG, 2022* Self-Supervised Super-Resolution for Multi-Exposure Push-Frame Satellites* DDRN -> Deep Distillation Recursive Network for Video Satellite Imagery Super-Resolution* worldstrat -> SISR and MISR implementations of SRCNN* MISR-GRU -> Pytorch implementation of MISR-GRU, a deep neural network for multi image super-resolution (MISR), for ProbaV Super Resolution Competition

Pansharpening

Image fusion of low res multispectral with high res pan band.* Several algorithms described in the ArcGIS docs, with the simplest being taking the mean of the pan and RGB pixel value.* For into to classical methods see this notebook and this kaggle kernel* rio-pansharpen -> pansharpening Landsat scenes* Simple-Pansharpening-Algorithms* Working-For-Pansharpening -> long list of pansharpening methods and update of Awesome-Pansharpening* PSGAN -> A Generative Adversarial Network for Remote Sensing Image Pan-sharpening, arxiv paper* Pansharpening-by-Convolutional-Neural-Network* PBRfilter -> {P}ansharpening by {B}ackground {R}emoval algorithm for sharpening RGB images* pypansharpening -> multiple algorithms implemented in python* Deep-Learning-PanSharpening -> deep-learning based pan-sharpening code package, we reimplemented include PNN, MSDCNN, PanNet, TFNet, SRPPNN, and our purposed network DIPNet* HyperTransformer -> A Textural and Spectral Feature Fusion Transformer for Pansharpening* DIP-HyperKite -> Hyperspectral Pansharpening Based on Improved Deep Image Prior and Residual Reconstruction* D2TNet -> code for 2022 paper: A ConvLSTM Network with Dual-direction Transfer for Pan-sharpening* PanColorGAN-VHR-Satellite-Images -> code for 2020 paper: Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs* MTLPANSEG -> code for 2019 paper: Multi-task deep learning for satellite image pansharpening and segmentation* Z-PNN -> code for 2022 paper: Pansharpening by convolutional neural networks in the full resolution framework* GTP-PNet -> code for 2021 paper: GTP-PNet: A residual learning network based on gradient transformation prior for pansharpening* UDL -> code for 2021 paper: Dynamic Cross Feature Fusion for Remote Sensing Pansharpening* PSData -> A Large-Scale General Pan-sharpening DataSet, which contains PSData3 (QB, GF-2, WV-3) and PSData4 (QB, GF-1, GF-2, WV-2).* AFPN -> Adaptive Detail Injection-Based Feature Pyramid Network For Pan-sharpening* pan-sharpening -> multiple methods demonstrated for multispectral and panchromatic images* PSGan-Family -> code for 2020 paper: PSGAN: A Generative Adversarial Network for Remote Sensing Image Pan-Sharpening* PanNet-Landsat -> code for 2017 paper: A Deep Network Architecture for Pan-Sharpening* DLPan-Toolbox -> code for 2022 paper: Machine Learning in Pansharpening: A Benchmark, from Shallow to Deep Networks* LPPN -> code for 2021 paper: Laplacian pyramid networks: A new approach for multispectral pansharpening* S2SSCCNN -> code for 2020 paper: Zero-shot Sentinel-2 Sharpening Using A Symmetric Skipped Connection Convolutional Neural Network* S2S_UCNN -> code for 2021 paper: Sentinel 2 sharpening using a single unsupervised convolutional neural network with MTF-Based degradation model* SSE-Net -> code for 2022 paper: Spatial and Spectral Extraction Network With Adaptive Feature Fusion for Pansharpening* UCGAN -> code for 2022 paper: Unsupervised Cycle-consistent Generative Adversarial Networks for Pan-sharpening* GCPNet -> code for 2022 paper: When Pansharpening Meets Graph Convolution Network and Knowledge Distillation* PanFormer -> code for 2022 paper: PanFormer: a Transformer Based Model for Pan-sharpening* Pansharpening -> code for 2021 paper: Pansformers: Transformer-Based Self-Attention Network for Pansharpening

Image-to-image translation

Translate images e.g. from SAR to RGB.* How to Develop a Pix2Pix GAN for Image-to-Image Translation -> how to develop a Pix2Pix model for translating satellite photographs to Google map images. A good intro to GANS* A growing problem of ‘deepfake geography’: How AI falsifies satellite images* Kaggle Pix2Pix Maps -> dataset for pix2pix to take a google map satellite photo and build a street map* guided-deep-decoder -> With guided deep decoder, you can solve different image pair fusion problems, allowing super-resolution, pansharpening or denoising* hackathon-ci-2020 -> generate nighttime imagery from infrared observations* satellite-to-satellite-translation -> VAE-GAN architecture for unsupervised image-to-image translation with shared spectral reconstruction loss. Model is trained on GOES-16/17 and Himawari-8 L1B data* Pytorch implementation of UNet for converting aerial satellite images into google maps kinda images* Seamless-Satellite-image-Synthesis -> generate abitrarily large RGB images from a map* How to Develop a Pix2Pix GAN for Image-to-Image Translation -> article on machinelearningmastery.com* Satellite-Imagery-to-Map-Translation-using-Pix2Pix-GAN-framework* RSITSRMISD -> PyTorch implementation of Remote sensing image translation via style-based recalibration module and improved style discriminator* pix2pixgooglemaps -> Converts satellite images to map images using pix2pix models* sar2color-igarss2018-chainer -> code for 2018 paper: Image Translation Between Sar and Optical Imagery with Generative Adversarial Nets* HSI2RGB -> Create realistic looking RGB images using remote sensing hyperspectral images* sattomap -> Learning mappings to generate city maps images from corresponding satellite images* pix2pix-GANs -> Generate Map using Satellite Image & PyTorch

GANS

GANS are famously used for generating synthetic data, see the section Synthetic data* Anomaly Detection on Mars using a GAN* Using Generative Adversarial Networks to Address Scarcity of Geospatial Training Data -> GAN perform better than CNN in segmenting land cover classes outside of the training dataset (article, no code)* Building-A-Nets -> robust building extraction from high-resolution remote sensing images with adversarial networks* GANmapper -> a building footprint generator using Generative Adversarial Networks* CSA-CDGAN -> Channel Self-Attention Based Generative Adversarial Network for Change Detection of Remote Sensing Images* DSGAN -> a conditinal GAN for dynamic precipitation downscaling* MarsGAN -> GAN trained on satellite photos of Mars* HC_ADGAN -> codes for the paper Adaptive Dropblock Enhanced GenerativeAdversarial Networks for Hyperspectral Image Classification* SCALAE -> code for our paper Formatting the Landscape: Spatial conditional GAN for varying population in satellite imagery. Method to generate satellite imagery from custom 2D population maps* Satellite-Image-Forgery-Detection-and-Localization* STGAN -> PyTorch Implementation of STGAN for Cloud Removal in Satellite Images, with paper* ds-gan-spatiotemporal-evaluation -> evaluating use of deep generative models in remote sensing applications* pub-ffi-gan -> code for 2018 paper: Applying generative adversarial networks for anomaly detection in hyperspectral remote sensing imagery* GAN-based method to generate high-resolution remote sensing for data augmentation and image classification* Remote-Sensing-Image-Generation -> Generate RS Images using Generative Adversarial Networks (GAN)* RoadDA -> code for 2021 paper: Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training for Road Segmentation of Remote Sensing Images* PSGan-Family -> code for 2020 paper: PSGAN: A Generative Adversarial Network for Remote Sensing Image Pan-Sharpening* Satellite Image Augmetation with GANs -> code for 2022 paper: Image Augmentation for Satellite Images

Transformers

Transformer-in-Remote-Sensing -> code for 2022 paper: Transformers in Remote Sensing: A Survey
Transformers in remote sensing -> blog post by robmarkcole
Remote-Sensing-RVSA -> code for 2022 paper: Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model
SatViT -> self-supervised training of multispectral optical and SAR vision transformers
UDAforRS -> code for 2022 paper: Unsupervised Domain Adaptation for Remote Sensing Semantic Segmentation with Transformer

Adversarial ML

Efforts to detect falsified images & deepfakes. Also checkout Synthetic data* UAE-RS -> dataset that provides black-box adversarial samples in the remote sensing field* PSGAN -> code for paper: Perturbation Seeking Generative Adversarial Networks: A Defense Framework for Remote Sensing Image Scene Classification* SACNet -> code for 2021 paper: Self-Attention Context Network: Addressing the Threat of Adversarial Attacks for Hyperspectral Image Classification

Autoencoders, dimensionality reduction, image embeddings & similarity search

Autoencoders & their Application in Remote Sensing -> intro article and example use case applied to SAR data for land classification
LEt-SNE -> Dimensionality Reduction and visualization technique that compensates for the curse of dimensionality
AutoEncoders for Land Cover Classification of Hyperspectral Images -> An autoencoder nerual net is used to reduce 103 band data to 60 features (dimensionality reduction), keras. Also read part 2 which implements K-NNC, SVM and Gradient Boosting
Image-Similarity-Search -> an app that helps perform super fast image retrieval on PyTorch models for better embedding space interpretability
Interactive-TSNE -> a tool that provides a way to visually view a PyTorch model's feature representation for better embedding space interpretability
How Airbus Detects Anomalies in ISS Telemetry Data Using TFX -> uses an autoencoder
RoofNet -> identify roof age using historical satellite images to lower the customer acquisition cost for new solar installations. Uses a VAE: Variational Autoencoder
Visual search over billions of aerial and satellite images -> implemented at Descartes labs
parallax -> Tool for interactive embeddings visualization
Deep-Gapfill -> Official implementation of Optical image gap filling using deep convolutional autoencoder from optical and radar images
Mxnet repository for generating embeddings on satellite images -> Includes sampling of images, mining algorithms, different architectures, error functions, measures for evaluation.
Fine tuning CLIP with Remote Sensing (Satellite) images and captions -> fine tuning CLIP on the RSICD image captioning dataset, to enable querying large catalogues in natural language. With repo, uses 🤗
Image search with 🤗 datasets -> tutorial on fine tuning an image search model
SynImageAnalysis -> comparing syn and real sattlelite images in the latent feature space (embeddings)
GRN-SNDL -> model the relations between samples (or scenes) by making use of a graph structure which is fed into network learning
SauMoCo -> codes for TGRS paper: Deep Unsupervised Embedding for Remotely Sensed Images Based on Spatially Augmented Momentum Contrast
TGRS_RiDe -> Rotation Invariant Deep Embedding for RemoteSensing Images
RaVAEn -> RaVAEn is a lightweight, unsupervised approach for change detection in satellite data based on Variational Auto-Encoders (VAEs) with the specific purpose of on-board deployment
Reverse image search using deep discrete feature extraction and locality-sensitive hashing
SNCA_CE -> code for the paper Deep Metric Learning based on Scalable Neighborhood Components for Remote Sensing Scene Characterization
LandslideDetection-from-satellite-imagery -> Using Attention and Autoencoder boosted CNN
split-brain-remote-sensing -> code for 2018 paper: Analysis of Color Space Quantization in Split-Brain Autoencoder for Remote Sensing Image Classification
image-similarity-measures -> Implementation of eight evaluation metrics to access the similarity between two images. Blog post here
LargeScaleGeoVisual_Search -> ResNet architecture on UC Merced Land Use Dataset with hamming distance for similarity based search
geobacter -> Generates useful feature embeddings for geospatial locations
Satellite-Image-Segmentation -> the KV-Net model uses this feature of autoencoders to reconnect the disconnected roads
Satellite-Image-Enhancement -> Image enhancement using GAN's and autoencoders
Variational-Autoencoder-For-Satellite-Imagery -> a special VAE to squeeze N images into one single representation with colors segmentating the different objects
DINCAE -> Data-Interpolating Convolutional Auto-Encoder is a neural network to reconstruct missing data in satellite observations
3DSITSClustering -> code for 2020 paper: Unsupervised Satellite Image Time Series Clustering Using Object-Based Approaches and 3D Convolutional Autoencoder
sat_cnn -> code for 2022 paper: Estimating Generalized Measures of Local Neighbourhood Context from Multispectral Satellite Images Using a Convolutional Neural Network. Uses a convolutional autoencoder (CAE)
you-are-here -> Matlab code for 2020 paper: You Are Here: Geolocation by Embedding Maps and Images
Tensorflow similarity -> offers state-of-the-art algorithms for metric learning and all the necessary components to research, train, evaluate, and serve similarity-based models
Train SimSiam on Satellite Images using Lightly to generate embeddings that can be used for data exploration and understanding
AirbusSDCdup -> Project focused on detecting duplicate regions of overlapping satellite imagery. Applied to Airbus ship detection dataset

Image retreival

DemoAHCLfor_TGRS2022 -> code for 2022 paper: Asymmetric Hash Code Learning (AHCL) for remote sensing image retreival
GaLR -> code for 2022 paper: Remote Sensing Cross-Modal Text-Image Retrieval Based on Global and Local Information
retrievalSystem -> cross-modal image retrieval system
AMFMN -> code for the 2021 paper: Exploring a Fine-grained Multiscale Method for Cross-modal Remote Sensing Image Retrieval
Active-Learning-for-Remote-Sensing-Image-Retrieval -> unofficial implementation of paper: A Novel Active Learning Method in Relevance Feedback for Content-Based Remote Sensing Image Retrieval
CMIR-NET -> code for 2020 paper: A deep learning based model for cross-modal retrieval in remote sensing
Deep-Hash-learning-for-Remote-Sensing-Image-Retrieval -> code for 2020 paper: Deep Hash Learning for Remote Sensing Image Retrieval
MHCLN -> code for 2018 paper: Deep Metric and Hash-Code Learning for Content-Based Retrieval of Remote Sensing Images
HydroViet_VOR -> Object Retrieval in satellite images with Triplet Network
AMFMN -> code for 2021 paper: Exploring a Fine-Grained Multiscale Method for Cross-Modal Remote Sensing Image Retrieval

Image Captioning & Visual Question Answering

See the section Image captioning datasets
remote-sensing-image-caption -> image classification and image caption by PyTorch
Fine tuning CLIP with Remote Sensing (Satellite) images and captions -> fine tuning CLIP on the RSICD image captioning dataset, to enable querying large catalogues in natural language. With repo, uses 🤗
VQA-easy2hard -> code for 2022 paper: From Easy to Hard: Learning Language-guided Curriculum for Visual Question Answering on Remote Sensing Data
CapFormer -> Pure transformer for remote sensing image caption
remotesensingimage_captioning -> code for 2019 paper: Region Driven Remote Sensing Image Captioning
Remote Sensing Image Captioning with Transformer and Multilabel Classification
Siamese-spatial-Graph-Convolution-Network -> code for 2019 paper: Siamese graph convolutional network for content based remote sensing image retrieval
MLAT -> code for 2022 paper: Remote-Sensing Image Captioning Based on Multilayer Aggregated Transformer
WordSent -> code for 2020 paper: Word–Sentence Framework for Remote Sensing Image Captioning
a-mask-guided-transformer-with-topic-token -> code for 2022 paper: A Mask-Guided Transformer Network with Topic Token for Remote Sensing Image Captioning
MetaCaptioning -> code for 2022 paper: Meta captioning: A meta learning based remote sensing image captioning framework
Transformer-for-image-captioning -> a transformer for image captioning, trained on the UCM dataset

Mixed data learning

These techniques combine multiple data types, e.g. imagery and text data.* Predicting the locations of traffic accidents with satellite imagery and convolutional neural networks -> Combining satellite imagery and structured data to predict the location of traffic accidents with a neural network of neural networks, with repo* Multi-Input Deep Neural Networks with PyTorch-Lightning - Combine Image and Tabular Data -> excellent intro article using pytorch, not actually applied to satellite data but to real estate data, with repo* Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps -> fusion based architectures and coarse-to-fine segmentation to include the OpenStreetMap layer into multispectral-based deep fully convolutional networks, arxiv paper* Composing Decision Forest and Neural Network models tensorflow documentation* pyimagesearch article on mixed-data* pytorch-widedeep -> A flexible package for multimodal-deep-learning to combine tabular data with text and images using Wide and Deep models in Pytorch* accidentRiskMap -> Inferring high-resolution traffic accident risk maps based on satellite imagery and GPS trajectories

Few-shot learning

This is a class of techniques which attempt to make predictions for classes with few, one or even zero examples provided during training. In zero shot learning (ZSL) the model is assisted by the provision of auxiliary information which typically consists of descriptions/semantic attributes/word embeddings for both the seen and unseen classes at train time (ref). These approaches are particularly relevant to remote sensing, where there may be many examples of common classes, but few or even zero examples for other classes of interest.* Unseen Land Cover Classification from High-Resolution Orthophotos Using Integration of Zero-Shot Learning and Convolutional Neural Networks* FSODM -> Official Code for paper "Few-shot Object Detection on Remote Sensing Images" on arxiv* Few-Shot Classification of Aerial Scene Images via Meta-Learning -> 2020 publication, a classification model that can quickly adapt to unseen categories using only a few labeled samples* Papers about Few-shot Learning / Meta-Learning on Remote Sensing* SPNet -> code for 2021 paper: Siamese-Prototype Network for Few-Shot Remote Sensing Image Scene Classification* MDL4OW -> code for 2020 paper: Few-Shot Hyperspectral Image Classification With Unknown Classes Using Multitask Deep Learning* P-CNN -> code for 2021 paper: Prototype-CNN for Few-Shot Object Detection in Remote Sensing Images* CIR-FSD-2022 -> code for 2022 paper: Context Information Refinement for Few-Shot Object Detection in Remote Sensing Images* IEEETNNLSGia-CFSL -> code for 2022 paper: Graph Information Aggregation Cross-Domain Few-Shot Learning for Hyperspectral Image Classification* TIP2022CMFSL -> code for 2022 paper: Few-shot Learning with Class-Covariance Metric for Hyperspectral Image Classification* sen12ms-human-few-shot-classifier -> code for paper: Humans are poor few-shot classifiers for Sentinel-2 land cover* S3Net -> code for 2022 paper: S3Net: Spectral–Spatial Siamese Network for Few-Shot Hyperspectral Image Classification* SiameseNet-for-few-shot-Hyperspectral-Classification -> code for 2020 paper: 3DCSN:SiameseNet-for-few-shot-Hyperspectral-Classification

Self-supervised, unsupervised & contrastive learning

These techniques use unlabelled datasets. Yann LeCun has described self/unsupervised learning as the 'base of the cake': If we think of our brain as a cake, then the cake base is unsupervised learning. The machine predicts any part of its input for any observed part, all without the use of labelled data. Supervised learning forms the icing on the cake, and reinforcement learning is the cherry on top.* Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data -> Seasonal Contrast (SeCo) is an effective pipeline to leverage unlabeled data for in-domain pre-training of remote sensing representations. Models trained with SeCo achieve better performance than their ImageNet pre-trained counterparts and state-of-the-art self-supervised learning methods on multiple downstream tasks. paper and repo* Unsupervised Learning for Land Cover Classification in Satellite Imagery* Tile2Vec: Unsupervised representation learning for spatially distributed data* Contrastive Sensor Fusion -> Code implementing Contrastive Sensor Fusion, an approach for unsupervised learning of multi-sensor representations targeted at remote sensing imagery* hyperspectral-autoencoders -> Tools for training and using unsupervised autoencoders and supervised deep learning classifiers for hyperspectral data, built on tensorflow. Autoencoders are unsupervised neural networks that are useful for a range of applications such as unsupervised feature learning and dimensionality reduction.* Sentinel-2 image clustering in python* MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification and code* A generalizable and accessible approach to machine learning with global satellite imagery nature publication -> MOSAIKS is designed to solve an unlimited number of tasks at planet-scale quickly using feature vectors, with repo. Also see mosaiks-api* contrastive-satellite -> Using contrastive learning to create embeddings from optical EuroSAT Satellite-2 imagery* Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Coding -> arxiv paper and code* Self-Supervised-Learner by spaceml-org -> train a classifier with fewer labeled examples needed using self-supervised learning, example applied to UC Merced land use dataset* deepsentinel -> a sentinel-1 and -2 self-supervised sensor fusion model for general purpose semantic embedding* contrastiveSSLshipdetection -> Contrastive self supervised learning for ship detection in Sentinel 2 images* geography-aware-ssl -> uses spatially aligned images over time to construct temporal positive pairs in contrastive learning and geo-location to design pre-text tasks* CNN-Supervised Classification -> Python code for self-supervised classification of remotely sensed imagery - part of the Deep Riverscapes project* clustimage -> a python package for unsupervised clustering of images* LandSurfaceClustering -> Land surface classification using remote sensing data with unsupervised machine learning (k-means)* K-Means Clustering for Surface Segmentation of Satellite Images* Sentinel-2 satellite imagery for crop classification using unsupervised clustering -> label groups of pixels based on temporal trends of their NDVI values* TheColorOutOfSpace -> Pytorch code for the paper "The color out of space: learning self-supervised representations for Earth Observation imagery" using the BigEarthNet dataset* Semantic segmentation of SAR images using a self supervised technique* STEGO -> Unsupervised Semantic Segmentation by Distilling Feature Correspondences, with paper* Unsupervised Segmentation of Hyperspectral Remote Sensing Images with Superpixels* SoundingEarth -> Self-supervised Audiovisual Representation Learning for Remote Sensing Data, uses the SoundingEarth Dataset* singleSceneSemSegTgrs2022 -> code for 2022 paper: Unsupervised Single-Scene Semantic Segmentation for Earth Observation* SSLRemoteSensing -> code for 2021 paper: Semantic Segmentation of Remote Sensing Images With Self-Supervised Multitask Representation Learning* CBT code for 2022 paper: Continual Barlow Twins: continual self-supervised learning for remote sensing semantic segmentation* Unsupervised Satellite Image Classification based on Partial Adversarial Domain Adaptation -> Code for course project* T2FTS -> code for 2022 paper: Teaching Teachers First and Then Student: Hierarchical Distillation to Improve Long-Tailed Object Recognition in Aerial Images* SSLTransformerRS -> code for 2022 paper: Self-supervised Vision Transformers for Land-cover Segmentation andClassification* DINO-MM -> code for 2022 paper: Self-supervised Vision Transformers for Joint SAR-optical Representation Learning* SSL4EO-S12 -> a large-scale dataset for self-supervised learning in Earth observation* SSL4EO-Review -> code for 2022 paper: Self-supervised Learning in Remote Sensing: A Review* transferlearningcspt -> code for 2022 paper: Consecutive Pretraining: A Knowledge Transfer Learning Strategy with Relevant Unlabeled Data for Remote Sensing Domain* OTL -> code for 2022 paper: Clustering-Based Representation Learning through Output Translation and Its Application to Remote-Sensing Images* Push-and-Pull-Network -> code for 2022 paper: Contrastive Learning for Fine-grained Ship Classification in Remote Sensing Images* visslexperiments -> Self-supervised Learning using Facebook VISSL on the RESISC-45 satellite imagery classification dataset* MS2A-Net -> code for 2022 paper: MS 2 A-Net: Multi-scale spectral-spatial association network for hyperspectral image clustering* UDAforRS -> code for paper: Unsupervised Domain Adaptation for Remote Sensing Semantic Segmentation with Transformer* MERLIN -> code for 2021 paper: As if by magic: self-supervised training of deep despeckling networks with MERLIN

Weakly & semi-supervised learning

These techniques use a partially annotated dataset* MARE -> self-supervised Multi-Attention REsu-net for semantic segmentation in remote sensing* SSGF-for-HRRS-scene-classification -> code for 2018 paper: A semi-supervised generative framework with deep learning features for high-resolution remote sensing image scene classification* SFGAN -> code for 2018 paper: Semantic-Fusion Gans for Semi-Supervised Satellite Image Classification* SSDAN -> code for 2021 paper: Multi-Source Semi-Supervised Domain Adaptation Network for Remote Sensing Scene Classification* HR-S2DML -> code for 2020 paper: High-Rankness Regularized Semi-Supervised Deep Metric Learning for Remote Sensing Imagery* Semantic Segmentation of Satellite Images Using Point Supervision* fcd -> code for 2021 paper: Fixed-Point GAN for Cloud Detection. A weakly-supervised approach, training with only image-level labels* weak-segmentation -> Weakly supervised semantic segmentation for aerial images in pytorch* TNNLS2022X-GPN -> Code for paper: Semisupervised Cross-scale Graph Prototypical Network for Hyperspectral Image Classification* weakly_supervised -> code for the paper Weakly Supervised Deep Learning for Segmentation of Remote Sensing Imagery. Demonstrates that segmentation can be performed using small datasets comprised of pixel or image labels* wan -> Weakly-Supervised Domain Adaptation for Built-up Region Segmentation in Aerial and Satellite Imagery, with arxiv paper* sourcerer -> A Bayesian-inspired deep learning method for semi-supervised domain adaptation designed for land cover mapping from satellite image time series (SITS). Paper* MSMatch -> Semi-Supervised Multispectral Scene Classification with Few Labels. Includes code to work with both the RGB and the multispectral (MS) versions of EuroSAT dataset and the UC Merced Land Use (UCM) dataset. Paper* Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning with arxiv paper* Semi-supervised learning in satellite image classification -> experimenting with MixMatch and the EuroSAT data set* ScRoadExtractor -> code for 2020 paper: Scribble-based Weakly Supervised Deep Learning for Road Surface Extraction from Remote Sensing Images* ICSS -> code for 2022 paper: Weakly-supervised continual learning for class-incremental segmentation* es-CP -> code for 2022 paper: Semi-Supervised Hyperspectral Image Classification Using a Probabilistic Pseudo-Label Generation Framework

Active learning

Supervised deep learning techniques typically require a huge number of annotated/labelled examples to provide a training dataset. However labelling at scale take significant time, expertise and resources. Active learning techniques aim to reduce the total amount of annotation that needs to be performed by selecting the most useful images to label from a large pool of unlabelled images, thus reducing the time to generate useful training datasets. These processes may be referred to as Human-in-the-Loop Machine Learning* Active learning for object detection in high-resolution satellite images -> arxiv paper* AIDE V2 - Tools for detecting wildlife in aerial images using active learning* AstronomicAL -> An interactive dashboard for visualisation, integration and classification of data using Active Learning* Read about active learning on the lightly platform and in label-studio* Active-Labeler by spaceml-org -> a CLI Tool that facilitates labeling datasets with just a SINGLE line of code* Labelling platform for Mapping Africa active learning project* ChangeDetectionProject -> Trying out Active Learning in with deep CNNs for Change detection on remote sensing data* ALS4GAN -> Active Learning for Improved Semi Supervised Semantic Segmentation in Satellite Images, with paper* Active-Learning-for-Remote-Sensing-Image-Retrieval -> unofficial implementation of paper: A Novel Active Learning Method in Relevance Feedback for Content-Based Remote Sensing Image Retrieval* DIAL -> code for 2022 paper: DIAL: Deep Interactive and Active Learning for Semantic Segmentation in Remote Sensing

Federated learning

Federated learning is a process for training models in a distributed fashion without sharing of data* Federated-Learning-for-Remote-Sensing -> implementation of three Federated Learning models

Image registration

Image registration is the process of registering one or more images onto another (typically well georeferenced) image. Traditionally this is performed manually by identifying control points (tie-points) in the images, for example using QGIS. This section lists approaches which mostly aim to automate this manual process. There is some overlap with the data fusion section but the distinction I make is that image registration is performed as a prerequisite to downstream processes which will use the registered data as an input.* Wikipedia article on registration -> register for change detection or image stitching* Phase correlation is used to estimate the XY translation between two images with sub-pixel accuracy. Can be used for accurate registration of low resolution imagery onto high resolution imagery, or to register a sub-image on a full image -> Unlike many spatial-domain algorithms, the phase correlation method is resilient to noise, occlusions, and other defects. With additional pre-processing image rotation and scale changes can also be calculated.* ImageRegistration -> Interview assignment for multimodal image registration using SIFT* imregdft -> Image registration using discrete Fourier transform. Given two images it can calculate the difference between scale, rotation and position of imaged features. Used by the up42 co-registration service* arosics -> Perform automatic subpixel co-registration of two satellite image datasets using phase-correlation, XY translations only.* SubpixelAlignment -> Implementation of tiff image alignment through phase correlation for pixel- and subpixel-bias* cnn-registration -> A image registration method using convolutional neural network features written in Python2, Tensorflow 1.5* Detecting Ground Control Points via Convolutional Neural Network for Stereo Matching -> code?* Image Registration: From SIFT to Deep Learning -> background reading on has the state of the art has evolved from OpenCV to Neural Networks* ImageCoregistration -> Image registration with openCV using sift and RANSAC* mapalignment -> Aligning and Updating Cadaster Maps with Remote Sensing Images* CVPR21-Deep-Lucas-Kanade-Homography -> deep learning pipeline to accurately align challenging multimodality images. The method is based on traditional Lucas-Kanade algorithm with feature maps extracted by deep neural networks.* eolearn implements phase correlation, feature matching and ECC* RStoolbox supports Image to Image Co-Registration based on Mutual Information* Reprojecting the Perseverance landing footage onto satellite imagery* Kornia provides image registration by gradient decent* LoFTR -> Detector-Free Local Feature Matching with Transformers. Good performance matching satellite image pairs, tryout the web demo on your data* image-to-db-registration -> This remote module implements an algorithm for automated vector Database registration onto an Image. Implemented in the orfeo-toolbox* MSHLMOregistration -> Multi-scale Histogram of Local Main Orientation for Remote Sensing Image Registration, with paper* cnn-matching -> code and datadset for paper: Deep learning algorithm for feature matching of cross modality remote sensing images* Imatch-P -> A demo using SuperGlue and SuperPoint to do the image matching task based PaddlePaddle* NBR-Net -> A Non-rigid Bi-directional Registration Network for Multi-temporal Remote Sensing Images* MU-Net -> code for paper: A Multi-Scale Framework with Unsupervised Learning for Remote Sensing Image Registration. Also checkout this implementation* unsupervisedDeepHomographyRAL2018 -> Unsupervised Deep Homography applied to aerial data* registrationcnnntg -> code for paper: A Multispectral Image Registration Method Based on Unsupervised Learning* remote-sensing-images-registration-dataset -> at 0.23m, 3.75m & 30m resolution* semantic-template-matching -> code for 2021 paper: A deep learning semantic template matching framework for remote sensing image registration* GMN-Generative-Matching-Network -> code for 2018 paper: Deep Generative Matching Network for Optical and SAR Image Registration* SOMatch -> code for 2020 paper: A deep learning framework for matching of SAR and optical imagery* Interspectral image registration dataset -> including satellite and drone imagery* RISG-image-matching -> A rotation invariant SuperGlue image matching algorithm* DeepAerialMatchingpytorch -> code for 2020 paper: A Two-Stream Symmetric Network with Bidirectional Ensemble for Aerial Image Matching* DPCN -> code for 2020 paper: Deep Phase Correlation for End-to-End Heterogeneous Sensor Measurements Matching* FSRA -> code for 2022 paper: A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization* IHN -> code for 2022 paper: Iterative Deep Homography Estimation* OSMNet -> code for 2021 paper: Explore Better Network Framework for High-Resolution Optical and SAR Image Matching* L2_Siamese -> code for the 2020 paper: Registration of Multiresolution Remote Sensing Images Based on L2-Siamese Model

Data fusion

Data fusion covers techniques which integrate multiple datasources, for example fusing SAR & optical to make predictions about crop type. It can also cover fusion with non imagery data such as IOT sensor data* Awesome-Data-Fusion-for-Remote-Sensing* UDALNGRSL -> Deep Unsupervised Blind Hyperspectral and Multispectral Data Fusion* CropTypeMapping -> Crop type mapping from optical and radar (Sentinel-1&2) time series using attention-based deep learning* Multimodal-Remote-Sensing-Toolkit -> uses Hyperspectral and LiDAR Data* Aerial-Template-Matching -> development of an algorithm for template Matching on aerial imagery applied to UAV dataset* DSUNet -> code for 2021 paper: Sentinel-1 and Sentinel-2 Data Fusion for Urban Change Detection using a Dual Stream U-Net, uses Onera Satellite Change Detection dataset* DDAUrbanExtraction -> Unsupervised Domain Adaptation for Global Urban Extraction using Sentinel-1 and Sentinel-2 Data* swinstfm -> code for paper: Remote Sensing Spatiotemporal Fusion using Swin Transformer* LoveCS -> code for 2022 paper: Cross-sensor domain adaptation for high-spatial resolution urban land-cover mapping: from airborne to spaceborne imagery* comingdowntoearth -> code for 2021 paper: Implementation of 'Coming Down to Earth: Satellite-to-Street View Synthesis for Geo-Localization'* Matching between acoustic and satellite images* MapRepair -> Deep Cadastre Maps Alignment and Temporal Inconsistencies Fix in Satellite Images* Compressive-Sensing-and-Deep-Learning-Framework -> Compressive Sensing is used as an initial guess to combine data from multiple sources, with LSTM used to refine the result* DeepSim -> code for paper: DeepSIM: GPS Spoofing Detection on UAVs using Satellite Imagery Matching* MHF-net -> code for 2019 paper: Multispectral and Hyperspectral Image Fusion by MS/HS Fusion Net* RemoteSensingImageFusion -> code for 2021 paper: Semi-Supervised Remote Sensing Image Fusion Using Multi-Scale Conditional Generative Adversarial network with Siamese Structure* CNNs for Multi-Source Remote Sensing Data Fusion -> code for 2021 paper: Single-stream CNN with Learnable Architecture for Multi-source Remote Sensing Data* Deep Generative Reflectance Fusion -> Achieving Landsat-like reflectance at any date by fusing Landsat and MODIS surface reflectance with deep generative models* IEEETGRSMDL-RS -> code for 2021 paper: More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification* SSRNET -> code for 2022 paper: SSR-NET: Spatial-Spectral Reconstruction Network for Hyperspectral and Multispectral Image Fusion* cross-view-image-matching -> code for 2019 paper: Bridging the Domain Gap for Ground-to-Aerial Image Matching* CoF-MSMG-PCNN -> code for 2020 paper: Remote Sensing Image Fusion via Boundary Measured Dual-Channel PCNN in Multi-Scale Morphological Gradient Domain* robustmatchingnetworkonremotesensingimagerypytorch -> code for 2019 paper: A Robust Matching Network for Gradually Estimating Geometric Transformation on Remote Sensing Imagery* edcstfn -> code for 2019 paper: An Enhanced Deep Convolutional Model for Spatiotemporal Image Fusion* ganstfm -> code for 2021 paper: A Flexible Reference-Insensitive Spatiotemporal Fusion Model for Remote Sensing Images Using Conditional Generative Adversarial Network* CMAFF -> code for 2021 paper: Cross-Modality Attentive Feature Fusion for Object Detection in Multispectral Remote Sensing Imagery* SOLC -> code for 2022 paper: MCANet: A joint semantic segmentation framework of optical and SAR images for land use classification. Uses WHU-OPT-SAR-dataset* MFT -> code for 2022 paper: Multimodal Fusion Transformer for Remote Sensing Image Classification* ISPRSS2FL -> code for 2021 paper: Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model* HSHT-Satellite-Imagery-Synthesis -> code for thesis - Improving Flood Maps by Increasing the Temporal Resolution of Satellites Using Hybrid Sensor Fusion* MDC -> code for 2021 paper: Unsupervised Data Fusion With Deeper Perspective: A Novel Multisensor Deep Clustering Algorithm* FusAtNet -> code for 2020 paper: FusAtNet: Dual Attention based SpectroSpatial Multimodal Fusion Network for Hyperspectral and LiDAR Classification* AMM-FuseNet -> code for 2022 paper: AMM-FuseNet: Attention-Based Multi-Modal Image Fusion Network for Land Cover Mapping* S1-S2_Transformer -> Sentinel-1 SAR and Sentinel-2 optical timeseries based Transformer architecture for tropical dry forest disturbance mapping* MANet -> code for 2022 paper: MANet: A Network Architecture for Remote Sensing Spatiotemporal Fusion Based on Multiscale and Attention Mechanisms* DCSA-Net -> code for 2022 paper: Dynamic Convolution Self-Attention Network for Land-Cover Classification in VHR Remote-Sensing Images

Terrain mapping, Disparity Estimation, Lidar, DEMs & NeRF

Measure surface contours & locate 3D points in space from 2D images. NeRF stands for Neural Radiance Fields and is the term used in deep learning communities to describe a model that generates views of complex 3D scenes based on a partial set of 2D images* Wikipedia DEM article and phase correlation article* Intro to depth from stereo* Map terrain from stereo images to produce a digital elevation model (DEM) -> high resolution & paired images required, typically 0.3 m, e.g. Worldview* Process of creating a DEM here* ArcGIS can generate DEMs from stereo images* https://github.com/MISS3D/s2p -> produces elevation models from images taken by high resolution optical satellites -> demo code on https://gfacciol.github.io/IS18/* Automatic 3D Reconstruction from Multi-Date Satellite Images* Predict the fate of glaciers* monodepth - Unsupervised single image depth prediction with CNNs* Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches* Terrain and hydrological analysis based on LiDAR-derived digital elevation models (DEM) - Python package* Phase correlation in scikit-image* s2p -> a Python library and command line tool that implements a stereo pipeline which produces elevation models from images taken by high resolution optical satellites such as Pléiades, WorldView, QuickBird, Spot or Ikonos* The Mapbox API provides images and elevation maps, article here* Reconstructing 3D buildings from aerial LiDAR with Mask R-CNN* ResDepth -> A Deep Prior For 3D Reconstruction From High-resolution Satellite Images* overhead-geopose-challenge -> competition to build computer vision algorithms that can effectively model the height and pose of ground objects for monocular satellite images taken from oblique angles. Blog post MEET THE WINNERS OF THE OVERHEAD GEOPOSE CHALLENGE* cars -> a dedicated and open source 3D tool to produce Digital Surface Models from satellite imaging by photogrammetry. This Multiview stereo pipeline is intended for massive DSM production with a robust and performant design* ImageToDEM -> Generating Elevation Surface from a Single RGB Remotely Sensed Image Using a U-Net for generator and a PatchGAN for the discriminator* IMELE -> Building Height Estimation from Single-View Aerial Imagery* ridges -> deep semantic segmentation model for identifying ridges in topography* planettools -> Selection of imagery from Planet API for creation of stereo elevation models* SatelliteNeRF -> PyTorch-based Neural Radiance Fields adapted to satellite domain* SatelliteSfM -> A library for solving the satellite structure from motion problem* SatelliteSurfaceReconstruction -> 3D Surface Reconstruction From Multi-Date Satellite Images, ISPRS, 2021* son2sat -> A neural network coded in TensorFlow 1 that produces satellite images from acoustic images* aerialmtl -> PyTorch implementation for multi-task learning with aerial images to learn both semantics and height from aerial image datasets; fuses RGB & lidar* ReKlaSat-3D -> 3D Reconstruction and Classification from Very High Resolution Satellite Imagery* M3Net -> A deep learning method for building height estimation using high-resolution multi-view imagery over urban areas* HMSM-Net -> code for 2022 paper: Hierarchical multi-scale matching network for disparity estimation of high-resolution satellite stereo images* StereoMatchingRemoteSensing -> code for 2021 paper: Dual-Scale Matching Network for Disparity Estimation of High-Resolution Remote Sensing Images* satnerf -> Learning Multi-View Satellite Photogrammetry With Transient Objects and Shadow Modeling Using RPC Cameras* SatMVS -> code for 2021 paper: Rational Polynomial Camera Model Warping for Deep Learning Based Satellite Multi-View Stereo Matching* ImpliCity -> reconstructs digital surface models (DSMs) from raw photogrammetric 3D point clouds and ortho-images with the help of an implicit neural 3D scene representation* WHU-Stereo -> a large-scale dataset for stereo matching of high-resolution satellite imagery & several deep learning methods for stereo matching. Methods include StereoNet, Pyramid Stereo Matching Network & HMSM-Net* Photogrammetry-Guide -> A guide covering Photogrammetry including the applications, libraries and tools that will make you a better and more efficient Photogrammetry development* DSM-to-DTM -> Exploring the use of machine learning to convert a Digital Surface Model (e.g. SRTM) to a Digital Terrain Model* GF-7StereoMatching -> code for paper: Large Scene DSM Generation of Gaofen-7 Imagery Combined with Deep Learning* Mapping drainage ditches in forested landscapes using deep learning and aerial laser scanning

Thermal Infrared

The World Needs (a lot) More Thermal Infrared Data from Space
IR2VI thermal-to-visible image translation framework based on GANs with code
The finest resolution urban outdoor heat exposure maps in major US cities -> urban microclimate modeling based on high resolution 3D urban models and meteorological data makes it possible to examine how people are exposed to heat stress at a fine spatio-temporal level.
ObjectClassificationinThermalImages -> classification accuracy was improved by adding the object size as a feature directly within the CNN
Thermal imaging with satellites blog post by Christoph Rieke
Object Detection on Thermal Images -> using YOLO-v3 and applied to a terrestrial dataset from FLIR, the article offers some usful insights into the model training
Fire alerts service by Descartes Labs
Background Invariant Classification on Infrared Imagery by Data Efficient Training and Reducing Bias in CNNs
CMAFF -> code for 2021 paper: Cross-Modality Attentive Feature Fusion for Object Detection in Multispectral Remote Sensing Imagery
LEO-GEO-landsurfacetemp -> Multi-sensor machine learning to retrieve high spatiotemporal resolution land surface temperature

SAR

See SAR

NDVI - vegetation index

Calculated via band math ndvi = np.true_divide((ir - r), (ir + r)) but challenging due to the size of the imagery
Example notebook local
Landsat data in cloud optimised (COG) format analysed for NDVI with medium article here.
Identifying Buildings in Satellite Images with Machine Learning and Quilt -> NDVI & edge detection via gaussian blur as features, fed to TPOT for training with labels from OpenStreetMap, modelled as a two class problem, “Buildings” and “Nature”
Seeing Through the Clouds - Predicting Vegetation Indices Using SAR
A walkthrough on calculating NDWI water index for flooded areas -> Derive zonal statistics from Sentinel 2 images using Rasterio and Geopandas
NDVI-Net -> code for 2020 paper: NDVI-Net: A fusion network for generating high-resolution normalized difference vegetation index in remote sensing
Awesome-Vegetation-Index
Remote-Sensing-Indices-Derivation-Tool -> Calculate spectral remote sensing indices from satellite imagery

General image quality

Convolutional autoencoder network can be employed to image denoising, read about this on the Keras blog
jitter-compensation -> Remote Sensing Image Jitter Detection and Compensation Using CNN
DeblurGANv2 -> Deblurring (Orders-of-Magnitude) Faster and Better
image-quality-assessment -> CNN to predict the aesthetic and technical quality of images
Convolutional autoencoder for image denoising -> keras guide
piq -> a collection of measures and metrics for image quality assessment
FFA-Net -> Feature Fusion Attention Network for Single Image Dehazing
DeepCalib -> A Deep Learning Approach for Automatic Intrinsic Calibration of Wide Field-of-View Cameras
PerceptualSimilarity -> LPIPS is a perceptual metric which aims to overcome the limitations of traditional metrics such as PSNR & SSIM, to better represent the features the human eye picks up on
Optical-RemoteSensing-Image-Resolution -> code for 2018 paper: Deep Memory Connected Neural Network for Optical Remote Sensing Image Restoration. Two applications: Gaussian image denoising and single image super-resolution
Hyperspectral-Deblurring-and-Destriping
HyDe -> Hyperspectral Denoising algorithm toolbox in Python, with paper
HLF-DIP -> code for 2022 paper: Unsupervised Hyperspectral Denoising Based on Deep Image Prior and Least Favorable Distribution
RQUNetVAE -> code for 2022 paper: Riesz-Quincunx-UNet Variational Auto-Encoder for Satellite Image Denoising
deep-hs-prior -> code for 2019 paper: Deep Hyperspectral Prior: Denoising, Inpainting, Super-Resolution
iquaflow -> from Satellogic, an image quality framework that aims at providing a set of tools to assess image quality by using the performance of AI models trained on the images as a proxy.

Synthetic data

Training data can be hard to acquire, particularly for rare events such as change detection after disasters, or imagery of rare classes of objects. In these situations, generating synthetic training data might be the only option. This has become quite sophisticated, with 3D models being use with open source games engines such as Unreal. * The Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentation with repo* RarePlanes -> incorporates both real and synthetically generated satellite imagery including aircraft. Read the arxiv paper and checkout this repo. Note the dataset is available through the AWS Open-Data Program for free download* Read this article from NVIDIA which discusses fine tuning a model pre-trained on synthetic data (Rareplanes) with 10% real data, then pruning the model to reduce its size, before quantizing the model to improve inference speed* Combining Synthetic Data with Real Data to Improve Detection Results in Satellite Imagery* BlenderGIS could be used for synthetic data generation* bifrost.ai -> simulated data service with geospatial output data formats* oktal-se -> software for generating simulated data across a wide range of bands including optical and SAR* The Nuances of Extracting Utility from Synthetic Data -> We find that strategically augmenting the real dataset is nearly as effective as adding synthetic data in the quest to improve the detection or rare object classes, and that fully extracting the utility of synthetic data is a nuanced process* Synthesizing Robustness -> explores how to best leverage and enhance synthetic data* rendered.ai -> The Platform as a Service for Creating Synthetic Data* syntheticxviewairplanes -> creation of airplanes synthetic dataset using ArcGIS CityEngine* Combining Synthetic Data with Real Data to Improve Detection Results in Satellite Imagery: Case Study* SynImageAnalysis -> comparing syn and real sattlelite images in the latent feature space (embeddings)* Import OpenStreetMap data into Unreal Engine 4* deepfake-satellite-images -> dataset that includes over 1M images of synthetic aerial images* synthetic-disaster -> Generate synthetic satellite images of natural disasters using deep neural networks* STPLS3D -> A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset* LESS -> LargE-Scale remote sensing data and image Simulation framework over heterogeneous 3D scenes* Synthesizing Robustness: Dataset Size Requirements and Geographic Insights -> Medium article, concludes that synthetic data is most beneficial to the rarest object classes and that extracting utility from synthetic data often takes significant effort and creativity* rsimgsynth -> code for 2020 paper: Synthesizing Optical and SAR Imagery From Land Cover Maps and Auxiliary Raster Data

Image dataset creation

Many datasets on kaggle & elsewhere have been created by screen-clipping Google Maps or browsing web portals. The tools below are to create datasets programatically* MapTilesDownloader -> A super easy to use map tiles downloader built using Python* jimutmap -> get enormous amount of high resolution satellite images from apple / google maps quickly through multi-threading* google-maps-downloader -> A short python script that downloads satellite imagery from Google Maps* ExtractSatelliteImagesFromCSV -> extract satellite images using a CSV file that contains latitude and longitude, uses mapbox* sentinelsat -> Search and download Copernicus Sentinel satellite images* SentinelDownloader -> a high level wrapper to the SentinelSat that provides an object oriented interface, asynchronous downloading, quickview & simpler searching methods* GEES2Downloader -> Downloader for GEE S2 bands* Sentinel-2 satellite tiles images downloader from Copernicus -> Minimizes data download and combines multiple tiles to return a single area of interest* felicette -> Satellite imagery for dummies. Generate JPEG earth imagery from coordinates/location name with publicly available satellite data* Easy Landsat Download* A simple python scrapper to get satellite images of Africa, Europe and Oceania's weather using the Sat24 website* RGISTools -> Tools for Downloading, Customizing, and Processing Time Series of Satellite Images from Landsat, MODIS, and Sentinel* DeepSatData -> Automatically create machine learning datasets from satellite images* landsat_ingestor -> Scripts and other artifacts for landsat data ingestion into Amazon public hosting* satpy -> a python library for reading and manipulating meteorological remote sensing data and writing it to various image and data file formats* GIBS-Downloader -> a command-line tool which facilitates the downloading of NASA satellite imagery and offers different functionalities in order to prepare the images for training in a machine learning pipeline* eodag -> Earth Observation Data Access Gateway* pylandsat -> Search, download, and preprocess Landsat imagery* landsatxplore -> Search and download Landsat scenes from EarthExplorer* OpenSarToolkit -> High-level functionality for the inventory, download and pre-processing of Sentinel-1 data in the python language* lsru -> Query and Order Landsat Surface Reflectance data via ESPA* eoreader -> Remote-sensing opensource python library reading optical and SAR sensors, loading and stacking bands, clouds, DEM and index in a sensor-agnostic way* Export thumbnails from Earth Engine* deepsentinel-osm -> A repository to generate land cover labels from OpenStreetMap* img2dataset -> Easily turn large sets of image urls to an image dataset. Can download, resize and package 100M urls in 20h on one machine* ohsome2label -> Historical OpenStreetMap (OSM) Objects to Machine Learning Training Samples* Label Maker -> a library for creating machine-learning ready data by pairing satellite images with OpenStreetMap (OSM) vector data* sentinel2tools -> downloading & basic processing of Sentinel 2 imagesry. Read Sentinel2tools: simple lib for downloading Sentinel-2 satellite images* Aerial-Satellite-Imagery-Retrieval -> A program using Bing maps tile system to automatically download Aerial / Satellite Imagery given a lat/lon bounding box and level of detail* google-maps-at-88-mph -> Google Maps keeps old satellite imagery around for a while – this tool collects what's available for a user-specified region in the form of a GIF* srtmDownloader -> Python library (multi-threaded) for retrieving SRTM elevation map of CGIAR-CSI* ImageDatasetViz -> create a mosaic of images in a dataset for previewing purposes* landsatlinks -> A simple CLI interface to generate download urls for Landsat Collection 2 Level 1 product bundles* pyeo -> a set of portable, extensible and modular Python scripts for machine learning in earth observation and GIS, including downloading, preprocessing, creation of base layers, classification and validation.* metaearth -> Download and access remote sensing data from any platform* geoget -> Download geodata for anywhere in Earth via ladsweb.modaps.eosdis.nasa.gov* geeml -> A python package to extract Google Earth Engine data for machine learning

Image chipping/tiling & merging

Since raw images can be very large, it is usually necessary to chip/tile them into smaller images before annotation & training* imageslicer -> Split images into tiles. Join the tiles back together* tiler by nuno-faria -> split images into tiles and merge tiles into a large image* tiler by the-lay -> N-dimensional NumPy array tiling and merging with overlapping, padding and tapering* xbatcher -> Xbatcher is a small library for iterating xarray DataArrays in batches. The goal is to make it easy to feed xarray datasets to machine learning libraries such as Keras* GeoTaggedImageChip -> A simple script to create geo tagged image chips from high resolution RS iamges for training deep learning models such as Unet* geotiff-crop-dataset -> A Pytorch Dataloader for tif image files that dynamically crops the image* Train-Test-Validation-Dataset-Generation -> app to crop images and create small patches of a large image e.g. Satellite/Aerial Images, which will then be used for training and testing Deep Learning models specifically semantic segmentation models* satproc -> Python library and CLI tools for processing geospatial imagery for ML* Sliding Window -> break large images into a series of smaller chunks* patchify -> A library that helps you split image into small, overlappable patches, and merge patches into original image* split-rs-data -> Divide remote sensing images and their labels into data sets of specified size* image-reconstructor-patches -> Reconstruct Image from Patches with a Variable Stride* rpccropper -> A small standalone tool to crop satellite images and their RPC* geotile -> python library for tiling the geographic raster data* GeoPatch -> generating patches from remote sensing data* ImageTilingUtils -> Minimalistic set of image reader agnostic tools to easily iterate over large images* splitraster -> Creates a tiled output from an input raster dataset. pip installable* SAHI -> Utilties for slicing COCO formatted annotations and image files, performing sliced inference using MMDetection, Detectron2, YOLOv5, HuggingFace detectors and calculating AP over image slices.

Annotation

For supervised machine learning, you will require annotated images. For example if you are performing object detection you will need to annotate images with bounding boxes. Check that your annotation tool of choice supports large image (likely geotiff) files, as not all will. Note that GeoJSON is widely used by remote sensing researchers but this annotation format is not commonly supported in general computer vision frameworks, and in practice you may have to convert the annotation format to use the data with your chosen framework. There are both closed and open source tools for creating and converting annotation formats. Some of these tools are simply for performing annotation, whilst others add features such as dataset management and versioning. Note that self-supervised and active learning approaches might circumvent the need to perform a large scale annotation exercise. Note that tiffs/geotiffs cannot be displayed by most browsers (Chrome), but CAN render in Safari.

Annotation tools with GEO features

GroundWork is designed for annotating and labeling geospatial data like satellite imagery, from Azavea
labelbox.com -> free tier is quite generous, supports annotating Geotiffs & returning annotations with geospatial coordinates. Watch this webcast
diffgram describes itself as a complete training data platform for machine learning delivered as a single application, supports streaming data to pytorch & tensorflow. COGS can be annotated
iris -> Tool for manual image segmentation and classification of satellite imagery
If you are considering building an in house annotation platform read this article. Used PostGis database, GeoJson format and GIS standard in a stateless architecture
satellite-imagery-labeling-tool -> from Microsoft, this is a lightweight web-interface for creating and sharing vector annotations over satellite/aerial imagery scenes
RSLabel -> remote sensing (RS) image annotation tool for deep learning
encord -> supports annotatin SAR
Flood-Annotation-Tool -> Annotation tool to annotate flooded and non-flooded regions on satellite image, uses jupyter notebook

Open source annotation tools

awesome-data-labeling -> long list of annotation tools
awesome-open-data-annotation -> another long list of annotation tools
labelImg is the classic desktop tool, limited to bounding boxes for object detection. Also checkout roLabelImg which supports ROTATED rectangle regions, as often occurs in aerial imagery. labelImg_OBB is another fork supporting orinted bounding boxes (OBB)
Labelme is a very popular & simple dektop app for polygonal annotation suitable for object detection and semantic segmentation. Note it outputs annotations in a custom LabelMe JSON format which you will need to convert, e.g. using labelme2coco. Read Labelme Image Annotation for Geotiffs
Label Studio is a multi-type data labeling and annotation tool with standardized output format, syncing to buckets, and supports importing pre-annotations (create with a model). Checkout label-studio-converter for converting Label Studio annotations into common dataset formats
CVAT suports object detection, segmentation and classification via a local web app. This article on Roboflow gives a good intro to CVAT. Checkout CVAT images validator
VoTT -> an electron app for building end to end Object Detection Models from Images and Videos, by Microsoft
Create your own annotation tool using Bokeh Holoviews, tkinter, or see these dash examples for object detection and segmentation
Deeplabel is a cross-platform tool for annotating images with labelled bounding boxes. Deeplabel also supports running inference using state-of-the-art object detection models like Faster-RCNN and YOLOv4. With support out-of-the-box for CUDA, you can quickly label an entire dataset using an existing model.
Alturos.ImageAnnotation is a collaborative tool for labeling image data on S3 for yolo
pigeonXT -> create custom image classification annotators within Jupyter notebooks
ipyannotations -> Image annotations in python using Jupyter notebooks
Label-Detect -> is a graphical image annotation tool and using this tool a user can also train and test large satellite images, fork of the popular labelImg tool
Swipe-Labeler -> Swipe Labeler is a Graphical User Interface based tool that allows rapid labeling of image data
dash_doodler -> A web application built with plotly/dash for image segmentation with minimal supervision
remo -> A webapp and Python library that lets you explore and control your image datasets
TensorFlow Object Detection API provides a handy utility for object annotation within Google Colab notebooks. See usage here
coco-annotator -> Web-based image segmentation tool for object detection, localization, and keypoints
pylabel -> Python library for computer vision labeling tasks. The core functionality is to translate bounding box annotations between different formats-for example, from coco to yolo. PyLabel also includes an image labeling tool that runs in a Jupyter notebook that can annotate images manually or perform automatic labeling using a pre-trained model
BMW-Labeltool-Lite -> bounding box annotator
django-labeller -> An image labelling tool for creating segmentation data sets, for Django and Flask
scalabel -> supports 2D images and 3D point clouds
Detection-Label-Tool -> Change detection and object annotation, uses PyQt
image_sorter -> A quick interface for sorting a folder of images into two other folders
bulk -> A Simple Bulk Labelling Tool using embeddings

Cloud hosted & paid annotation tools & services

Several open source tools are also available on the cloud, including CVAT, label-studio & Diffgram. In general cloud solutions will provide a lot of infrastructure and storage for you, as well as integration with outsourced annotators.* GroundWork is designed for annotating and labeling geospatial data like satellite imagery, from Azavea* labelbox.com -> free tier is quite generous, supports annotating Geotiffs & returning annotations with geospatial coordinates. Watch this webcast* Roboflow -> in addition to annotation this platform makes it easy to convert between annotation formats & manage datasets, as well as train and deploy custom models to private API endpoints. Read How to Train Computer Vision Models on Aerial Imagery* supervise.ly is one of the more fully featured platforms, decent free tier* AWS supports image annotation via the Rekognition Custom Labels console* rectlabel is a desktop app for MacOS to annotate images for bounding box object detection and segmentation, paid and free (rectlabel-lite) versions* hasty.ai -> supports model assisted annotation & inferencing

Annotation formats

Note there are many annotation formats, although PASCAL VOC and coco-json are the most commonly used. I recommend using geojson for storing polygons, then converting these to the required format when needed.* PASCAL VOC format: XML files in the format used by ImageNet* coco-json format: JSON in the format used by the 2015 COCO dataset* YOLO Darknet TXT format: contains one text file per image, used by YOLO* Tensorflow TFRecord: a proprietary binary file format used by the Tensorflow Object Detection API* Many more formats listed here* OBB: orinted bounding boxes are polygons representing rotated rectangles

Annotation visualisation & conversion tools

Tools to visualise annotations & convert between formats. Note that most annotation software will allow you to visualise existing annotations* Dataset-Converters -> a conversion toolset between different object detection and instance segmentation annotation formats* FiftyOne -> open-source tool for building high quality datasets and computer vision models. Visualise labels, evaluate model predictions, explore scenarios of interest, identify failure modes, find annotation mistakes, and much more! Read Nearest Neighbor Embeddings Search with Qdrant and FiftyOne* rebox -> Easily convert between bounding box annotation formats* Pascal VOC BBox Viewer* COCO-Assistant -> Helper for dealing with MS-COCO annotations; Merge datasets, Remove specfic category from dataset, Generate annotations statistics - distribution of object areas and category distribution* pybboxes -> Light weight toolkit for bounding boxes providing conversion between bounding box types and simple computations* voc2coco -> Convert VOC format XMLs to COCO format json* ObjectDetectionEval -> Parse all kinds of object detection databases (ImageNet, COCO, YOLO, PascalVOC, OpenImage, CVAT, LabelMe, etc.) & save to other formats* LabelMeYoloConverter -> Convert LabelMe Annotation Tool JSON format to YOLO text file format* mask-to-polygons -> Routines for extracting and working with polygons from semantic segmentation masks* labelme2coco -> Converts LabelMe JSON format into COCO object detection and instance segmentation format.

Training models

This section discusses training machine learning models.

Metrics

A number of metrics are common to all model types (but can have slightly different meanings in contexts such as object detection), whilst other metrics are very specific to particular classes of model. The correct choice of metric is particularly critical for imbalanced dataset problems, e.g. object detection* TP = true positive, FP = false positive, TN = true negative, FN = false negative* Precision is the % of correct positive predictions, calculated as precision = TP/(TP+FP)* Recall or true positive rate (TPR), is the % of true positives captured by the model, calculated as recall = TP/(TP+FN). Note that FN is not possible in object detection, so recall is not appropriate.* The F1 score (also called the F-score or the F-measure) is the harmonic mean of precision and recall, calculated as F1 = 2*(precision * recall)/(precision + recall). It conveys the balance between the precision and the recall. Ref* The false positive rate (FPR), calculated as FPR = FP/(FP+TN) is often plotted against recall/TPR in an ROC curve which shows how the TPR/FPR tradeoff varies with classification threshold. Lowering the classification threshold returns more true positives, but also more false positives. Note that since FN is not possible in object detection, ROC curves are not appropriate.* Precision-vs-recall curves visualise the tradeoff between making false positives and false negatives* Accuracy is the most commonly used metric in 'real life' but can be a highly misleading metric for imbalanced data sets.* IoU is an object detection specific metric, being the average intersect over union of prediction and ground truth bounding boxes for a given confidence threshold* mAP@0.5 is another object detection specific metric, being the mean value of the average precision for each class. @0.5 sets a threshold for how much of the predicted bounding box overlaps the ground truth bounding box, i.e. "minimum 50% overlap"* For more comprehensive definitions checkout Object-Detection-Metrics* Metrics to Evaluate your Semantic Segmentation Model

ML best practice

This section includes tips and ideas I have picked up from other practitioners including ai-fast-track, FraPochetti & the IceVision community* Almost all imagery data on the internet is in RGB format, and common techniques designed for working with this 3 band imagery may fail or need significant adaptation to work with multiband data (e.g. 13-band Sentinel 2)* In general, classification and object detection models are created using transfer learning, where the majority of the weights are not updated in training but have been pre computed using standard vision datasets such as ImageNet* Since satellite images are typically very large, it is common to tile them before processing. Alternatively checkout Fully Convolutional Image Classification on Arbitrary Sized Image -> TLDR replace the fully-connected layer with a convolution-layer* Where you have small sample sizes, e.g. for a small object class which may be under represented in your training dataset, use image augmentation* In general, larger models will outperform smaller models, particularly on challenging tasks such as detecting small objetcs* If model performance in unsatisfactory, try to increase your dataset size before switching to another model architecture* In training, whenever possible increase the batch size, as small batch sizes produce poor normalization statistics* The vast majority of the literature uses supervised learning with the requirement for large volumes of annotated data, which is a bottleneck to development and deployment. We are just starting to see self-supervised approaches applied to remote sensing data* 4-ways-to-improve-class-imbalance discusses the pros and cons of several rebalancing techniques, applied to an aerial dataset. Reason to read: models can reach an accuracy ceiling where majority classes are easily predicted but minority classes poorly predicted. Overall model accuracy may not improve until steps are taken to account for class imbalance.* For general guidance on dataset size see this issue* Read A Recipe for Training Neural Networks by Andrej Karpathy* Seven steps towards a satellite imagery dataset* Implementing Transfer Learning from RGB to Multi-channel Imagery -> takes a resnet50 model pre-trained on an input of 224x224 pixels with 3 channels (RGB) and updates it for a new input of 480x400 pixels and 15 channels (12 new + RGB) using keras* How to implement augmentations for Multispectral Satellite Images Segmentation using Fastai-v2 and Albumentations* Principal Component Analysis: In-depth understanding through image visualization applied to Landsat TM images, with repo* Leveraging Geolocation Data for Machine Learning: Essential Techniques -> A Gentle Guide to Feature Engineering and Visualization with Geospatial data, in Plain English* 3 Tips to Optimize Your Machine Learning Project for Data Labeling* Image Classification Labeling: Single Class versus Multiple Class Projects* Labeling Satellite Imagery for Machine Learning* Image Augmentations for Aerial Datasets* Leveraging satellite imagery for machine learning computer vision applications* Best Practices for Preparing and Augmenting Image Data for CNNs* Using TensorBoard While Training Land Cover Models with Satellite Imagery* An Overview of Model Compression Techniques for Deep Learning in Space* Visualise Embeddings with Tensorboard -> also checkout the Tensorflow Embedding Projector* Introduction to Satellite Image Augmentation with Generative Adversarial Networks - video* Use Gradio and W&B together to monitor training and view predictions* Every important satellite imagery analysis project is challenging, but here are ten straightforward steps to get started* Challenges with SpaceNet 4 off-nadir satellite imagery: Look angle and target azimuth angle -> building prediction in images taken at nearly identical look angles — for example, 29 and 30 degrees — produced radically different performance scores.* How not to test your deep learning algorithm? - bad ideas to avoid* AI products and remote sensing: yes, it is hard and yes, you need a good infra -> advice on building an in-house data annotation service* Boosting object detection performance through ensembling on satellite imagery* How to use deep learning on satellite imagery — Playing with the loss function* On the importance of proper data handling* Generate SSD anchor box aspect ratios using k-means clustering -> tutorial showing how to discover a set of aspect ratios that are custom-fit for your dataset, applied to tensorflow object detection* Transfer Learning on Greyscale Images: How to Fine-Tune Pretrained Models on Black-and-White Datasets* How to create a DataBlock for Multispectral Satellite Image Segmentation with the Fastai* A comprehensive list of ML and AI acronyms and abbreviations* Finding an optimal number of “K” classes for unsupervised classification on Remote Sensing Data -> i.e 'elbow' method* Supplement your training data with 'negative' examples which are created through random selection of regions of the image that contain no objects of interest, read Setting a Foundation for Machine Learning* The law of diminishing returns often applies to dataset size, read Quantifying the Effects of Resolution on Image Classification Accuracy* Implementing Transfer Learning from RGB to Multi-channel Imagery -> Medium article which discusses how to convert a model trained on 3 channels to more channels, adding an additional 12 channels to the original 3 channel RGB image, uses Keras* satellite-segmentation-pytorch -> explores a wide variety of image augmentations to increase training dataset size* Quantifying uncertainty in deep learning systems* How to create a custom Dataset / Loader in PyTorch, from Scratch, for multi-band Satellite Images Dataset from Kaggle -> uses the 38-Cloud dataset* How To Normalize Satellite Images for Deep Learning* ML Tooling 2022 by developmentseed

Free online compute

A GPU is required for training deep learning models (but not necessarily for inferencing), and this section lists a couple of free Jupyter environments with GPU available. There is a good overview of online Jupyter development environments on the fastai site. I personally use Colab Pro with data hosted on Google Drive, or Sagemaker if I have very long running training jobs.

Google Colab

Collaboratory notebooks with GPU as a backend for free for 12 hours at a time. Note that the GPU may be shared with other users, so if you aren't getting good performance try reloading.
Also a pro tier for $10 a month -> https://colab.research.google.com/signup
Tensorflow, pytorch & fastai available but you may need to update them
Colab Alive is a chrome extension that keeps Colab notebooks alive.
colab-ssh -> lets you ssh to a colab instance like it’s an EC2 machine and install packages that require full linux functionality

Kaggle - also Google!

Free to use
GPU Kernels - may run for 1 hour
Tensorflow, pytorch & fastai available but you may need to update them
Advantage that many datasets are already available

Others

Paperspace gradient -> free tier includes GPU usage
Deepnote -> many features for collaboration, GPU use is paid

Deep learning projects & frameworks

TorchGeo -> a PyTorch domain library providing datasets, samplers, transforms, and pre-trained models specific to geospatial data, supported by Microsoft. Read Geospatial deep learning with TorchGeo
rastervision -> An open source Python framework for building computer vision models on aerial, satellite, and other large imagery sets
torchrs -> PyTorch implementation of popular datasets and models in remote sensing tasksenhance) -> Enhance PyTorch vision for semantic segmentation, multi-channel images and TIF file
DeepHyperX -> A Python/pytorch tool to perform deep learning experiments on various hyperspectral datasets
DELTA -> Deep Earth Learning, Tools, and Analysis, by NASA is a framework for deep learning on satellite imagery, based on Tensorflow & using MLflow for tracking experiments
Lightly is a computer vision framework for training deep learning models using self-supervised learning
Icevision offers a curated collection of hundreds of high-quality pre-trained models within an easy to use framework
pytorch_eo -> aims to make Deep Learning for Earth Observation data easy and accessible to real-world cases and research alike
NGVEO -> applying convolutional neural networks (CNN) to Earth Observation (EO) data from Sentinel 1 and 2 using python and PyTorch
chip-n-scale-queue-arranger by developmentseed -> an orchestration pipeline for running machine learning inference at scale. Supports fastai models
http://spaceml.org/ -> A Machine Learning toolbox and developer community building the next generation AI applications for space science and exploration
TorchSat is an open-source deep learning framework for satellite imagery analysis based on PyTorch (no activity since June 2020)
DeepNetsForEO -> Uses SegNET for working on remote sensing images using deep learning (no activity since 2019)
RoboSat -> semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water, clouds (no longer maintained)
DeepOSM -> Train a deep learning net with OpenStreetMap features and satellite imagery (no activity since 2017)
mapwith.ai -> AI assisted mapping of roads with OpenStreetMap. Part of Open-Mapping-At-Facebook
SAHI -> Python library for slicing image datasets, performing sliced inference with MMDetection, Detectron2, YOLOv5, Torchvision detectors and generating error analysis plots. Read the arxiv paper and article SAHI: A vision library for large-scale object detection & instance segmentation
terragpu -> Python library to process and classify remote sensing imagery by means of GPUs and AI/ML
EOTorchLoader -> Pytorch dataloader and pytorch lightning datamodule for Earth Observation imagery
satellighte -> an image classification library that consist state-of-the-art deep learning methods, using PyTorch Lightning
aeronetlib -> Python library to work with geospatial raster and vector data for deep learning
rsi-semantic-segmentation -> A unified PyTorch framework for semantic segmentation from remote sensing imagery
AiTLAS -> implements state-of-the-art AI methods for exploratory and predictive analysis of satellite images
mmsegmentation -> Semantic Segmentation Toolbox with support for many remote sensing datasets including LoveDA, Potsdam, Vaihingen & iSAID
ODEON landcover -> a set of command-line tools performing semantic segmentation on remote sensing images (aerial and/or satellite) with as many layers as you wish
aitlas-arena -> An open-source benchmark framework for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO)
PaddleRS -> remote sensing image processing development kit
RocketML Deep Neural Networks -> read Satellite Image Classification using rmldnn and Sentinel 2 data
segmentation_gym -> A neural gym for training deep learning models to carry out geoscientific image segmentation
EarthNets -> includes a database of 400 baseline models, and tutorial examples of common deep learning tasks on satellite imagery. Accompanied by a paper: EarthNets: Empowering AI in Earth Observation

Neural nets in space

Processing on board a satellite allows less data to be downlinked. e.g. super-resolution image might take 8 images to generate, then a single image is downlinked. Other applications include cloud detection and collision avoidance.* Lockheed Martin and USC to Launch Jetson-Based Nanosatellite for Scientific Research Into Orbit - Aug 2020 - One app that will run on the GPU-accelerated satellite is SuperRes, an AI-based application developed by Lockheed Martin, that can automatically enhance the quality of an image.* Intel to place movidius in orbit to filter images of clouds at source - Oct 2020 - Getting rid of these images before they’re even transmitted means that the satellite can actually realize a bandwidth savings of up to 30%* Whilst not involving neural nets the PyCubed project gets a mention here as it is putting python on space hardware such as the V-R3x* WorldFloods will pioneer the detection of global flood events from space, launched on June 30, 2021. This paper describes the model which is run on Intel Movidius Myriad2 hardware capable of processing a 12 MP image in less than a minute* How AI and machine learning can support spacecraft docking with repo uwing Yolov3* exo-space -> startup with plans to release an AI hardware addon for satellites* Sony’s Spresense microcontroller board is going to space -> vision applications include cloud detection, more details here* Ororatech Early Detection of Wildfires From Space -> OroraTech is launching its own AI nanosatellites with the NVIDIA Jetson Xavier NX system onboard* Palantir Edge AI in Space -> using NVIDIA Jetson for ship/aircraft/cloud detection & land cover segmentation* Spiral Blue -> startup building edge computers to run AI analytics on-board satellites* RaVAEn -> a lightweight, unsupervised approach for change detection in satellite data based on Variational Auto-Encoders (VAEs) with the specific purpose of on-board deployment. It flags changed areas to prioritise for downlink, shortening the response time

Courses

Introduction to Geospatial Raster and Vector Data with Python -> an intro course on a single page
Manning: Monitoring Changes in Surface Water Using Satellite Image Data
Automating GIS processes includes a lesson on automating raster data processing
For deep learning checkout the fastai course which uses the fastai library & pytorch
pyimagesearch.com hosts courses and plenty of material using opencv and keras
Official opencv courses on opencv.org
TensorFlow Developer Professional Certificate
GeospatialPythonCourseV1 -> a collection of blog posts turned into a course format
Satellite Machine Learning Training -> lessons on how to apply Machine Learning analysis to satellite data
DL-for-satellite-image-analysis -> short and minimalistic examples covering fundamentals of Deep Learning for Satellite Image Analysis using Jupyter notebooks, created by lakmalnd
Machine Learning on Earth Observation: ML4EO Bootcamp
Deep Learning DIY
UvA Deep Learning Tutorials
Practical Data Science Specialization -> AWS specific, develop and scale your data science projects into the cloud using Amazon SageMaker
Disaster Risk Monitoring Using Satellite Imagery by NVIDIA
Course materials for: Geospatial Data Science
Data-Science-For-Beginners -> by Microsoft
AI-For-Beginners -> by Microsoft
Remote Sensing Tutorials -> by the Canada Centre for Mapping and Earth Observation
RUS Copernicus Training
Practical Deep Learning for Coders -> the popular course by Jeremy Howard using fast.ai
engn3903 -> Environmental Sensing, Mapping and Modelling
PyTorch Fundamentals -> 4 hour course by Microsoft
pytorch-deep-learning -> Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course

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Table of contents

Techniques

Classification

Segmentation

Segmentation - Land use & land cover

Segmentation - Vegetation, crops & crop boundaries

Segmentation - Water, coastlines & floods

Segmentation - Fire, smoke & burn areas

Segmentation - Landslides

Segmentation - Glaciers

Segmentation - Other environmental

Segmentation - Roads

Segmentation - Buildings & rooftops

Segmentation - Solar panels

Segmentation - Other manmade

Instance segmentation

Panoptic segmentation

Object detection

Object counting

Object detection with rotated bounding boxes

Object detection enhanced by super resolution

Salient object detection

Object detection - Buildings, rooftops & solar panels

Object detection - Ships & boats

Object detection - Cars, vehicles & trains

Object detection - Planes & aircraft

Object detection - Infrastructure & utilities

Object detection - Animals

Object tracking in videos

Counting trees

Oil storage tank detection & oil spills

Cloud detection & removal

Change detection

Time series

Crop yield

Wealth and economic activity

Disaster response

Weather phenomena

Super-resolution

Single image super-resolution (SISR)

Multi image super-resolution (MISR)

Pansharpening

Image-to-image translation

GANS

Transformers

Adversarial ML

Autoencoders, dimensionality reduction, image embeddings & similarity search

Image retreival

Image Captioning & Visual Question Answering

Mixed data learning

Few-shot learning

Self-supervised, unsupervised & contrastive learning

Weakly & semi-supervised learning

Active learning

Federated learning

Image registration

Data fusion

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